**Meet the editor**

Olga Petrucci is a Researcher of the Research Institute for Geo-Hydrological Protection (IRPI) working in Cosenza Section of the Institute (Italy). She works on both triggering conditions and effects caused by damaging hydrogeological phenomena as landslides and floods. She carried out several researches on the occurrence of these phenomena, thus realizing a data-

base which currently is made of more than 11,000 records describing the occurrence, during the last two Centuries, of damaging landslides and floods in Calabria (Italy). The results of historical researches that have been published reveal the occurrence of not widely known hydrogeological phenomena, thus contributing to the dissemination of information on them. Moreover, she has been working on the impact of these phenomena, even elaborating damage assessment methodologies. She is currently in charge of the Historical Archive of IRPI Section of Cosenza and is author of several articles published on international journals and books also available on Google Books.

Contents

**Preface VII**

**to Flood 1**

**Natural Disaster 31**

William T. Robinson

**from Iran 95**

Chapter 1 **Vulnerability of Reinforced Concrete Structures Subjected**

Natarajan Chidambarathanu and Remya Retnan

Chapter 2 **Multi-Tier Networks for Citywide Damage Monitoring in a**

Takahiro Fujiwara and Takashi Watanabe

Peter H. Calkins and Ngu Wah Win

Darabi H., Zafari H. and Milani Nia S.

Chapter 3 **Impact of Hurricane Katrina on the Louisiana HIV/AIDS Epidemic: A Socio-Ecological Perspective 53**

Chapter 4 **Impacts of Cyclone Nargis on Social Capital and Happiness in Slightly and Heavily Affected Areas of Myanmar 71**

Chapter 5 **Participation in Natural Disaster Reconstruction, Lessons**

## Contents

#### **Preface XI**


Preface

ed areas.

in post-disaster reconstruction.

their failure during flood.

This book is an overview of the complex and multifaceted topic of natural disasters im‐ pact. When a natural disaster affects an urbanized sector it can destroy or heavily damage physical elements laying in the area, causing loss, social and economic disruption, and en‐ vironmental damage. At the same time, the unexpected alterations of the socio-economic environment can have several both short- and long-term effects on people health. A large number of possible approaches can be undertaken in order to assess economic, psycholog‐ ical, societal or environmental damage, aiming to reduce the effects of future disasters on the whole of these sectors. Moreover, the study approaches must be different according to the differences of grassroots characterizing social and cultural environments of the affect‐

This book proposes a range of studies realized in different continents, showing various aspects from which natural disasters can be view, thus giving the measure of the complex‐ ity and multidisciplinary of the topic. It starts with a paper presenting a possible strategy to either avoid or reduce the vulnerability of concrete buildings during floods. Then, it continues with an insight into the communication during post-disaster emergency phase and with two chapters concerning the assessment of two different kinds of impact on peo‐ ple everyday life. The book ends with an analysis of the role of stakeholder participation

**Chapter 1**, by *Chidambarathanu and Retnam*, deals with prevention of damage related to floods, one of the most destructive type of natural disasters that every year cause huge economic damage and victims, especially in countries where urban settlements have been expanding dangerously near to rivers, without undertaking any kind of precautionary measure. The chapter focuses on the incorporation of flood loads during the design stage of reinforced concrete buildings and the assessment of flood vulnerability of these build‐ ings. The study, carried out with reference to the Indian constructive standard code, aims to find out the flood vulnerability limit as a factor of ground floor height under flood forces, and to quantify flood load. Three different frame models are analyzed, and the re‐ sults are plotted to compare the effects of flood forces in each type of frames. In a preven‐ tion perspective, aiming to recognize actions that can reduce vulnerability of reinforced concrete buildings before possible harm occurs, the importance of the outcome arises from the need of a strengthening solution to the new or existing structures in order to avoid

**Chapter 2**, by *Fujiwara and Watanabe*, firstly reviews some networking technologies for dis‐ aster communications, and then discusses a scheme of multi-tier damage monitoring in a citywide area during natural disasters. It shows the scheme of the centralized hierarchical

## Preface

This book is an overview of the complex and multifaceted topic of natural disasters im‐ pact. When a natural disaster affects an urbanized sector it can destroy or heavily damage physical elements laying in the area, causing loss, social and economic disruption, and en‐ vironmental damage. At the same time, the unexpected alterations of the socio-economic environment can have several both short- and long-term effects on people health. A large number of possible approaches can be undertaken in order to assess economic, psycholog‐ ical, societal or environmental damage, aiming to reduce the effects of future disasters on the whole of these sectors. Moreover, the study approaches must be different according to the differences of grassroots characterizing social and cultural environments of the affect‐ ed areas.

This book proposes a range of studies realized in different continents, showing various aspects from which natural disasters can be view, thus giving the measure of the complex‐ ity and multidisciplinary of the topic. It starts with a paper presenting a possible strategy to either avoid or reduce the vulnerability of concrete buildings during floods. Then, it continues with an insight into the communication during post-disaster emergency phase and with two chapters concerning the assessment of two different kinds of impact on peo‐ ple everyday life. The book ends with an analysis of the role of stakeholder participation in post-disaster reconstruction.

**Chapter 1**, by *Chidambarathanu and Retnam*, deals with prevention of damage related to floods, one of the most destructive type of natural disasters that every year cause huge economic damage and victims, especially in countries where urban settlements have been expanding dangerously near to rivers, without undertaking any kind of precautionary measure. The chapter focuses on the incorporation of flood loads during the design stage of reinforced concrete buildings and the assessment of flood vulnerability of these build‐ ings. The study, carried out with reference to the Indian constructive standard code, aims to find out the flood vulnerability limit as a factor of ground floor height under flood forces, and to quantify flood load. Three different frame models are analyzed, and the re‐ sults are plotted to compare the effects of flood forces in each type of frames. In a preven‐ tion perspective, aiming to recognize actions that can reduce vulnerability of reinforced concrete buildings before possible harm occurs, the importance of the outcome arises from the need of a strengthening solution to the new or existing structures in order to avoid their failure during flood.

**Chapter 2**, by *Fujiwara and Watanabe*, firstly reviews some networking technologies for dis‐ aster communications, and then discusses a scheme of multi-tier damage monitoring in a citywide area during natural disasters. It shows the scheme of the centralized hierarchical

network and the experimental system designed for dedicated damage monitoring. The monitoring system is capable of collecting information within one minute from 256,000 terminals deployed in a whole city. Thereby, the system is useful and effective to collect data quickly and stably in conditions where the links could be maintained. Based on both the concept of the centralized hierarchical network and the experimental results, it is shown that the hybrid wireless monitoring system enhanced with ad hoc networks. The experiments by computer simulation show as the network is capable of improving reacha‐ bility of packets, even in the damaged conditions of natural disasters. The chapter shows system configured with a centralized hierarchical network, which was developed to ac‐ quire damage information from lifeline facilities installed in residences. Some results of computer simulation for multi-tier networks enhanced with an ad hoc networking techni‐ que are also presented

reconstruction policies applied in the case of the three Iranian earthquakes analyzed shows that even though facing with both criticisms and obstacles, participation presents several advantages making it attractive for governments and reconstruction managers. Moreover, for the specific case of Iran, despite of limited participation, the attitudes have been changing over the time. One of the results of people participation is for example work division that leads to eliminate or strongly reduce conflicts between organizations,

(National Research Council-Research Institute for Geo-Hydrological Protection),

**Olga Petrucci** CNR-IRPI

Italy

Preface IX

thus speeding reconstruction process and avoiding parallel or duplicated works.

In **Chapter 3**, *Robinson* analyses the impacts that were observed after Hurricane Katrina (23 August 2005) on the population and individuals who are living with or at risk for HIV/ AIDS in Louisiana and the New Orleans area. These findings are also interpreted in the light of the Socio-Ecological Model of Health, in order to conceptualize how a major disas‐ ter like Katrina can have long reaching impacts on not just the individual but on entire communities and systems under which people live. The study highlights how a disaster can interfere with the large population living with HIV/AIDS or those who may be at risk for HIV in Louisiana. This numerous group of people can be seen as more vulnerable and may be disproportionately affected, thus it has been critical to examine the impact of the storm on the epidemic.

**Chapter 4**, by *Calkins and Win,* shows an attempt to measure the subjective, intangible im‐ pacts of the cyclone Nargis (Myanmar, 2 May, 2008) on the happiness and well-being of the inhabitants of the affected area. The goal is to infer, using two study areas differential‐ ly affected, its impacts on the level and distribution of well-being and social capital, and to make policy recommendations for the alleviation of some of the psychic effects. In fact, having no baseline of well-being of the heavily affected area, a slightly affected area was selected as a proxy benchmark for the "before" situation, against which the "after" situa‐ tion, 27 months following Nargis, is then compared. The results show that the happiness of the heavily affected area is not significantly different from that of the slightly-affected area, suggesting that human beings rebound rapidly from disasters. For both areas, spiri‐ tual happiness is more than twice as important as emotional happiness, and physical hap‐ piness is less than one-third as important as spiritual happiness. The study demonstrated that the impacts of natural disasters tend to strengthen social capital, while assistance and aid, if poorly administered, can undermine it. Social capital can play a strategic role in rehabilitating organizational performance, farm productivity, and mental health following a disaster. Furthermore, the informal safety net during disasters plays a pivotal role in helping people to access the resources such as credit.

Finally, **Chapter 5**, by *Darabi et al.,* presents a detailed analysis of the post-disaster recon‐ structions management in the case of three earthquakes occurred in Iran, stressing a bare‐ ly explored topic: the role of stakeholder participation to reconstruction. Starting from these cases study, the chapter presents a panoramic framework of the different and com‐ plex post disaster strategies used for reconstruction after major natural disasters, high‐ lighting the multi-faceted, multi-scale and uncertain process which involve and affect multiple actors and agencies for long periods after the disaster strikes. The review of the reconstruction policies applied in the case of the three Iranian earthquakes analyzed shows that even though facing with both criticisms and obstacles, participation presents several advantages making it attractive for governments and reconstruction managers. Moreover, for the specific case of Iran, despite of limited participation, the attitudes have been changing over the time. One of the results of people participation is for example work division that leads to eliminate or strongly reduce conflicts between organizations, thus speeding reconstruction process and avoiding parallel or duplicated works.

network and the experimental system designed for dedicated damage monitoring. The monitoring system is capable of collecting information within one minute from 256,000 terminals deployed in a whole city. Thereby, the system is useful and effective to collect data quickly and stably in conditions where the links could be maintained. Based on both the concept of the centralized hierarchical network and the experimental results, it is shown that the hybrid wireless monitoring system enhanced with ad hoc networks. The experiments by computer simulation show as the network is capable of improving reacha‐ bility of packets, even in the damaged conditions of natural disasters. The chapter shows system configured with a centralized hierarchical network, which was developed to ac‐ quire damage information from lifeline facilities installed in residences. Some results of computer simulation for multi-tier networks enhanced with an ad hoc networking techni‐

In **Chapter 3**, *Robinson* analyses the impacts that were observed after Hurricane Katrina (23 August 2005) on the population and individuals who are living with or at risk for HIV/ AIDS in Louisiana and the New Orleans area. These findings are also interpreted in the light of the Socio-Ecological Model of Health, in order to conceptualize how a major disas‐ ter like Katrina can have long reaching impacts on not just the individual but on entire communities and systems under which people live. The study highlights how a disaster can interfere with the large population living with HIV/AIDS or those who may be at risk for HIV in Louisiana. This numerous group of people can be seen as more vulnerable and may be disproportionately affected, thus it has been critical to examine the impact of the

**Chapter 4**, by *Calkins and Win,* shows an attempt to measure the subjective, intangible im‐ pacts of the cyclone Nargis (Myanmar, 2 May, 2008) on the happiness and well-being of the inhabitants of the affected area. The goal is to infer, using two study areas differential‐ ly affected, its impacts on the level and distribution of well-being and social capital, and to make policy recommendations for the alleviation of some of the psychic effects. In fact, having no baseline of well-being of the heavily affected area, a slightly affected area was selected as a proxy benchmark for the "before" situation, against which the "after" situa‐ tion, 27 months following Nargis, is then compared. The results show that the happiness of the heavily affected area is not significantly different from that of the slightly-affected area, suggesting that human beings rebound rapidly from disasters. For both areas, spiri‐ tual happiness is more than twice as important as emotional happiness, and physical hap‐ piness is less than one-third as important as spiritual happiness. The study demonstrated that the impacts of natural disasters tend to strengthen social capital, while assistance and aid, if poorly administered, can undermine it. Social capital can play a strategic role in rehabilitating organizational performance, farm productivity, and mental health following a disaster. Furthermore, the informal safety net during disasters plays a pivotal role in

Finally, **Chapter 5**, by *Darabi et al.,* presents a detailed analysis of the post-disaster recon‐ structions management in the case of three earthquakes occurred in Iran, stressing a bare‐ ly explored topic: the role of stakeholder participation to reconstruction. Starting from these cases study, the chapter presents a panoramic framework of the different and com‐ plex post disaster strategies used for reconstruction after major natural disasters, high‐ lighting the multi-faceted, multi-scale and uncertain process which involve and affect multiple actors and agencies for long periods after the disaster strikes. The review of the

que are also presented

VIII Preface

storm on the epidemic.

helping people to access the resources such as credit.

**Olga Petrucci** CNR-IRPI (National Research Council-Research Institute for Geo-Hydrological Protection), Italy

**Chapter 1**

**Vulnerability of Reinforced Concrete Structures**

Floods are one of the most widespread and destructive natural disasters occurring in the world (Singh and Sharma 2009), and with the increase in constructions along river courses and concentration of population around floodplain areas, flood-induced damages have been continuously increasing. The annual disaster record reveals that flood occurrence increased about ten folds over the past five decades (Scheuren et al. 2007). Thus, floods are posing a great threat and challenge to planers, design engineers, insurance industries, policy makers, and to

Structural and non-structural measures can be used to deal with floods (Sagala 2006). Struc‐ tural measures include a set of works aiming to reduce one or more hydraulic parameters like runoff volume, peak discharge, rise in water level, duration of flood, flow velocity, etc. Nonstructural measures involve a wide range of measures to reduce flood risk through flood forecasting and early warning systems, emergency plans, and posing land use regulations and policies. The futuristic reinforced concrete buildings can be considered as a symbol of modern civilization. These buildings are usually constructed based on the guide lines given by the standard code books (like IS:456:2000, for India). Unfortunately, the code provisions consider the seismic loads and wind effects alone, while accounting the dead and live design loads, and exclude the flood loads. This implies the necessity to bring out corrective measures that can

This chapter focuses on both the incorporation of flood loads during the design stage and the assessment of flood vulnerability of reinforced concrete buildings. Vulnerability is expressed as a fraction of ground floor height and assumes that flood water at most immerse the building up to ground floor level. The importance of the outcome arises from the need of a strengthening

and reproduction in any medium, provided the original work is properly cited.

© 2013 Chidambarathanu and Retnan; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is

© 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

Natarajan Chidambarathanu and Remya Retnan

be adopted to reduce vulnerability before harm occurrences.

properly cited.

solution to avoid failure of new or existing structures during floods.

Additional information is available at the end of the chapter

**Subjected to Flood**

http://dx.doi.org/10.5772/53879

**1. Introduction**

the governments.

## **Vulnerability of Reinforced Concrete Structures Subjected to Flood**

Natarajan Chidambarathanu and Remya Retnan

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/53879

### **1. Introduction**

Floods are one of the most widespread and destructive natural disasters occurring in the world (Singh and Sharma 2009), and with the increase in constructions along river courses and concentration of population around floodplain areas, flood-induced damages have been continuously increasing. The annual disaster record reveals that flood occurrence increased about ten folds over the past five decades (Scheuren et al. 2007). Thus, floods are posing a great threat and challenge to planers, design engineers, insurance industries, policy makers, and to the governments.

Structural and non-structural measures can be used to deal with floods (Sagala 2006). Struc‐ tural measures include a set of works aiming to reduce one or more hydraulic parameters like runoff volume, peak discharge, rise in water level, duration of flood, flow velocity, etc. Nonstructural measures involve a wide range of measures to reduce flood risk through flood forecasting and early warning systems, emergency plans, and posing land use regulations and policies. The futuristic reinforced concrete buildings can be considered as a symbol of modern civilization. These buildings are usually constructed based on the guide lines given by the standard code books (like IS:456:2000, for India). Unfortunately, the code provisions consider the seismic loads and wind effects alone, while accounting the dead and live design loads, and exclude the flood loads. This implies the necessity to bring out corrective measures that can be adopted to reduce vulnerability before harm occurrences.

This chapter focuses on both the incorporation of flood loads during the design stage and the assessment of flood vulnerability of reinforced concrete buildings. Vulnerability is expressed as a fraction of ground floor height and assumes that flood water at most immerse the building up to ground floor level. The importance of the outcome arises from the need of a strengthening solution to avoid failure of new or existing structures during floods.

properly cited.

© 2013 Chidambarathanu and Retnan; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **1.1. Forces due to flood**

The physical forces which act on the buildings include hydrostatic loads (Fig.1.), hydrody‐ namic loads (Fig.2.), and impact loads, and these loads can be exacerbated by the effects of water scouring soil from around and below the foundation (FEMA, 2001). The *hydrostatic loads* are both lateral (pressures) and vertical (buoyant) in nature. The lateral forces result from differences in interior and exterior water surface elevations. As the floodwaters rise, the higher water on the exterior of the building acts inward against the walls of the building. Sufficient lateral pressures may cause permanent deflections and damage to structural elements within the building. The buoyant forces are the vertical uplift of the structure due to the displacement of water, just as a boat displaces water causing it to float. These uplift forces may be the result of the actual building materials (the floating nature of wood products), or due to air on the interior of a tightly built structure. When the buoyant forces associated with the flood exceed the weight of the building components and the connections to the foundation system, the structure may float from its foundation.

The water flowing around the building during a flood creates *hydrodynamic loads* on the structure. These loads are the frontal impact loads from the upstream flow, the drag on the sides of the building, and the suction on the rear face of the building as the floodwaters flow around the structure. The magnitude of the hydrodynamic loads depends on both the velocity of water and the shape of the structure. Like the hydrostatic pressures, these lateral pressures may cause the collapsing of either structural walls or floors.

**Figure 2.** Schematic sketch of hydrodynamic force (FEMA, 2001)

**2. Literature review**

inside and outside a residence.

concentrated loads to the structural elements of the building.

Impact loads during floods may be the direct forces associated with waves, as typically encountered during coastal flooding, or the impact of debris floating in the waters, including logs, building components, and even vehicles. Impact loads can be destructive because the forces associated with them may be an order of magnitude higher than the hydrostatic and hydrodynamic. Floating debris can have devastating effects, as they apply large and/or

Vulnerability of Reinforced Concrete Structures Subjected to Flood

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3

FEMA (2001) published a manual focusing on the retrofitting of family residences subject to flooding without wave action. The measures include elevation of the structure in place, relocation of the structure, construction of barriers, dry flood proofing and wet flood proofing.

Kelman (2002), in a dissertation on Physical Flood Vulnerability of Residential Properties in Coastal Eastern England, examined the lateral pressure from flood differential depth between

The analyses necessary to determine flood-related hazard factors are also presented.

**Figure 1.** Schematic sketch of hydrostatic force (FEMA, 2001)

Vulnerability of Reinforced Concrete Structures Subjected to Flood http://dx.doi.org/10.5772/53879 3

**Figure 2.** Schematic sketch of hydrodynamic force (FEMA, 2001)

Impact loads during floods may be the direct forces associated with waves, as typically encountered during coastal flooding, or the impact of debris floating in the waters, including logs, building components, and even vehicles. Impact loads can be destructive because the forces associated with them may be an order of magnitude higher than the hydrostatic and hydrodynamic. Floating debris can have devastating effects, as they apply large and/or concentrated loads to the structural elements of the building.

#### **2. Literature review**

**1.1. Forces due to flood**

structure may float from its foundation.

may cause the collapsing of either structural walls or floors.

2 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**Figure 1.** Schematic sketch of hydrostatic force (FEMA, 2001)

The physical forces which act on the buildings include hydrostatic loads (Fig.1.), hydrody‐ namic loads (Fig.2.), and impact loads, and these loads can be exacerbated by the effects of water scouring soil from around and below the foundation (FEMA, 2001). The *hydrostatic loads* are both lateral (pressures) and vertical (buoyant) in nature. The lateral forces result from differences in interior and exterior water surface elevations. As the floodwaters rise, the higher water on the exterior of the building acts inward against the walls of the building. Sufficient lateral pressures may cause permanent deflections and damage to structural elements within the building. The buoyant forces are the vertical uplift of the structure due to the displacement of water, just as a boat displaces water causing it to float. These uplift forces may be the result of the actual building materials (the floating nature of wood products), or due to air on the interior of a tightly built structure. When the buoyant forces associated with the flood exceed the weight of the building components and the connections to the foundation system, the

The water flowing around the building during a flood creates *hydrodynamic loads* on the structure. These loads are the frontal impact loads from the upstream flow, the drag on the sides of the building, and the suction on the rear face of the building as the floodwaters flow around the structure. The magnitude of the hydrodynamic loads depends on both the velocity of water and the shape of the structure. Like the hydrostatic pressures, these lateral pressures

> FEMA (2001) published a manual focusing on the retrofitting of family residences subject to flooding without wave action. The measures include elevation of the structure in place, relocation of the structure, construction of barriers, dry flood proofing and wet flood proofing. The analyses necessary to determine flood-related hazard factors are also presented.

> Kelman (2002), in a dissertation on Physical Flood Vulnerability of Residential Properties in Coastal Eastern England, examined the lateral pressure from flood differential depth between inside and outside a residence.

Kelman and Spenc (2004) categorised flood actions on buildings as energy transfers, forces, pressures, or the consequences of water or contaminant contact.

Messener and Meyer (2005) argued that the challenge consists in understanding the interre‐ lations and social dynamics of flood risk perception, preparedness, vulnerability, flood damage and flood management, and to take this into account in a modern design of damage analysis and risk management.

Sagala (2006) examines the physical vulnerability to flood and people's coping mechanisms in flood prone residential areas in Naga city of Philippines. Six structural types of buildings were chosen and for each type of vulnerability curves (flood depth/damage) were plotted. Results indicate that buildings with plywood walls and wooden floors are the most vulnerable while the type with hollow block walls and concrete floors is the least vulnerable.

Arulselvan et al. (2007) conducted an experimental investigation on the influence of brick masonry infill in a reinforced cement concrete frame and validated outcomes by comparing them with theoretical results obtained by finite element analysis. Until the cracks developed in the infill, the contribution of infill to both stiffness and lateral stiffness was found to be very significant. The strains measured in infilled beams and columns were 20% less than bare frame beams up to failure of brick walls.

**Figure 3.** The steps of the methodology

**Figure 4.** Plan of considered building

The building configuration used for the study is regular, with plan dimensions 9m×18m. Table 1 lists the data associated with a four storey reinforced concrete building considered for the analysis, while the plan and elevation of the building are shown in Fig.4. and Fig.5., respec‐

Vulnerability of Reinforced Concrete Structures Subjected to Flood

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5

tively. In Fig.4., the direction of interest refers the perpendicular direction of flood.

**3.1. Building details**

Haugen and Kaynia (2008) presented a method for prediction of damage in a structure impacted by a debris flow of known magnitude. The method uses the principles of dynamic response of structures to earthquake excitation, and fragility curves proposed in HAZUS for estimation of the structural vulnerability, by the damage state probability. The model was tested on a debris flow in Italy and it gave probabilities between 34% and 66% for reaching the damage levels which actually occurred for five out of six structures.

Kreibich et al. (2009) investigated the importance of flow velocity, water depth and combina‐ tions of these two parameters on various types of damages to buildings and roads. A significant influence of flow velocity on damage to roads was found, in contrast to a minor influence on monetary losses and business interruption. The energy head is suggested as a suitable flood impact parameter for reliable forecasting of structural damage to residential buildings.

Lopez et al. (2010) developed a methodology to estimate flood vulnerability to buildings, in either riverine or coastal settings, based on the aggregated damage to individual building components. Building vulnerability is modelled based on analytical representations of the failure mechanisms of individual building components.

#### **3. Methodology**

The present work focuses on the assessment of flood physical vulnerability of building expressed as a factor of ground floor height. The influence of design variation zones or boundary conditions has been also investigated. The methodology is schematized in Fig.3.

Vulnerability of Reinforced Concrete Structures Subjected to Flood http://dx.doi.org/10.5772/53879 5

**Figure 3.** The steps of the methodology

#### **3.1. Building details**

Kelman and Spenc (2004) categorised flood actions on buildings as energy transfers, forces,

Messener and Meyer (2005) argued that the challenge consists in understanding the interre‐ lations and social dynamics of flood risk perception, preparedness, vulnerability, flood damage and flood management, and to take this into account in a modern design of damage

Sagala (2006) examines the physical vulnerability to flood and people's coping mechanisms in flood prone residential areas in Naga city of Philippines. Six structural types of buildings were chosen and for each type of vulnerability curves (flood depth/damage) were plotted. Results indicate that buildings with plywood walls and wooden floors are the most vulnerable while

Arulselvan et al. (2007) conducted an experimental investigation on the influence of brick masonry infill in a reinforced cement concrete frame and validated outcomes by comparing them with theoretical results obtained by finite element analysis. Until the cracks developed in the infill, the contribution of infill to both stiffness and lateral stiffness was found to be very significant. The strains measured in infilled beams and columns were 20% less than bare frame

Haugen and Kaynia (2008) presented a method for prediction of damage in a structure impacted by a debris flow of known magnitude. The method uses the principles of dynamic response of structures to earthquake excitation, and fragility curves proposed in HAZUS for estimation of the structural vulnerability, by the damage state probability. The model was tested on a debris flow in Italy and it gave probabilities between 34% and 66% for reaching the

Kreibich et al. (2009) investigated the importance of flow velocity, water depth and combina‐ tions of these two parameters on various types of damages to buildings and roads. A significant influence of flow velocity on damage to roads was found, in contrast to a minor influence on monetary losses and business interruption. The energy head is suggested as a suitable flood impact parameter for reliable forecasting of structural damage to residential buildings.

Lopez et al. (2010) developed a methodology to estimate flood vulnerability to buildings, in either riverine or coastal settings, based on the aggregated damage to individual building components. Building vulnerability is modelled based on analytical representations of the

The present work focuses on the assessment of flood physical vulnerability of building expressed as a factor of ground floor height. The influence of design variation zones or boundary conditions has been also investigated. The methodology is schematized in Fig.3.

the type with hollow block walls and concrete floors is the least vulnerable.

damage levels which actually occurred for five out of six structures.

failure mechanisms of individual building components.

pressures, or the consequences of water or contaminant contact.

4 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

analysis and risk management.

beams up to failure of brick walls.

**3. Methodology**

The building configuration used for the study is regular, with plan dimensions 9m×18m. Table 1 lists the data associated with a four storey reinforced concrete building considered for the analysis, while the plan and elevation of the building are shown in Fig.4. and Fig.5., respec‐ tively. In Fig.4., the direction of interest refers the perpendicular direction of flood.

**Figure 4.** Plan of considered building

**3.2. Modelling**

N/mm2

**Figure 6.** Bare frame SAP model

**Figure 7.** Frame with structural infill walls

To compute the critical effect, the flood was assumed to act along the 18m side and an intermediate 2D frame along 9m side was considered for the study. Three frame models were used, a) bare frame model, without any partition walls (Fig. 6.); b) frame with light weight partition wall; c) frame with structural infill wall (Fig. 7.). The infill walls were modelled as a diagonal strut having width 230mm, very low moment of inertia, modulus of elasticity 13800

negligible. Hence, frame models for both bare frame and frame with light weight partition walls were similar but the difference will come in to the picture while applying flood load.

and Poisson ratio 0.25. The weight of light weight partition walls were considered

Vulnerability of Reinforced Concrete Structures Subjected to Flood

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7

#### **Figure 5.** Elevation of frame


**Table 1.** Reinforced concrete building details

#### **3.2. Modelling**

**Figure 5.** Elevation of frame

6 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

4m 3m

6 3

3m in both X and Y directions

300mmx300mm 250mmx300mm 230mm 120mm

25 kN/m3 20 kN/m3

2×108 kN/m2 415 N/mm2

25×106 kN/m2

13.8×106 kN/m2

0.2 20 N/mm2

0.25

0.5kN/m2 1.5kN/m2

1.5kN/m2 3kN/m2

Ground floor height Remaining floors height

Bay width

Column size Beam size

No. of bays in X direction No. of bays in Y direction

Masonry wall thickness Slab thickness

Elastic modulus of steel Yield strength of steel

Unit weight of the concrete Unit weight of masonry

Young's modulus of concrete Poisson ratio of concrete Compressive strength of concrete

Young's modulus of masonry Poisson ratio of masonry

Terrace water proofing (TWF) load

**Table 1.** Reinforced concrete building details

Floor finish load

Live load on roof Live load on floor To compute the critical effect, the flood was assumed to act along the 18m side and an intermediate 2D frame along 9m side was considered for the study. Three frame models were used, a) bare frame model, without any partition walls (Fig. 6.); b) frame with light weight partition wall; c) frame with structural infill wall (Fig. 7.). The infill walls were modelled as a diagonal strut having width 230mm, very low moment of inertia, modulus of elasticity 13800 N/mm2 and Poisson ratio 0.25. The weight of light weight partition walls were considered negligible. Hence, frame models for both bare frame and frame with light weight partition walls were similar but the difference will come in to the picture while applying flood load.

**Figure 6.** Bare frame SAP model

**Figure 7.** Frame with structural infill walls

#### **3.3. Analysis**

The procedure consists of linear static and linear dynamic analysis. When the linear static or dynamic procedures are used for seismic evaluation, the design seismic forces, the distribution of applied loads over the height of the buildings, and the corresponding displacements are determined using a linearly elastic analysis. The various steps involved in SAP model analysis are the following:

loads consist of both lateral pressures and buoyancy forces. Lateral pressure is calculated using

meters. Since lateral hydrostatic loads are acting as triangular loads, the resultant hydrostatic

if the building is surrounded by water or in submerged condition. Here, the flood is considered as slow moving; hence the effect of buoyancy is neglected. Impact loads are velocity dependent loads. As no codes or design books are available for incorporating the impact effects, the magnitude of these loads is arbitrarily considered as a factor of hydrostatic force acting laterally as UDL over the surface. Table 3 shows the magnitude of flood loads acting on the column for

2 5.89 0.59 1.18 3 13.24 0.88 1.77 4 23.54 1.18 2.35

The flood loads are assumed as dynamic loads by considering the duration of flood td. The dynamic displacement and dynamic flood moment are found using a deformation response factor (R). R is the ratio dynamic to static displacement caused by the flood force. The dynamic

), where γ = 9.81 kN/m³ for water, and hf is the water depth in

Vulnerability of Reinforced Concrete Structures Subjected to Flood

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**Impact UDL (kN/m)** 0.1γh<sup>f</sup> 0.2γh<sup>f</sup>

/3 distance from ground level. Buoyancy force has a significant effect either

the formula Ps= γ h<sup>f</sup>

the frame models.

**hf (m) Ff (kN)**

**Table 3.** Flood loads on frame models

flood load is assumed as a rectangular pulse (Fig.8.).

**Figure 8.** a) SDF system (b) Rectangular pulse load (Chopra, 2009)

The governing equation is:

) acts at hf

load (Ff

(in kN/m2


The load combinations considered for the study are:

a) 1.5 (DL + IL) b) 1.2 (DL + IL ± EL)

c) 1.5 (DL ± EL) d) 0.9 DL ± 1.5 EL

Analyses were carried out for six different conditions of seismic zones, flood duration, flood water height, flood forces, frame models, and support conditions, to obtain the maximum design moment, flood moment and lateral displacements.

#### **3.4. Calculation of design moment**

The earthquake load calculations were made for all the zones and all the models analysed, and designed for IS 456:2000. Here, the earthquake zones are considered to demonstrate the different structural variations but not the multi-hazard conditions (Table 2). The design moment is lower for fixed support condition than hinged condition.


**Table 2.** Zone factor (Ref. IS 1893-2002)

#### **3.5. Calculation of flood loads**

Flood loads are assumed to act as: a) hydrostatic loads; b) impact loads as equivalent static loads; c) impact loads as dynamic loads, considering the duration of flood. The hydrostatic loads consist of both lateral pressures and buoyancy forces. Lateral pressure is calculated using the formula Ps= γ h<sup>f</sup> (in kN/m2 ), where γ = 9.81 kN/m³ for water, and hf is the water depth in meters. Since lateral hydrostatic loads are acting as triangular loads, the resultant hydrostatic load (Ff ) acts at hf /3 distance from ground level. Buoyancy force has a significant effect either if the building is surrounded by water or in submerged condition. Here, the flood is considered as slow moving; hence the effect of buoyancy is neglected. Impact loads are velocity dependent loads. As no codes or design books are available for incorporating the impact effects, the magnitude of these loads is arbitrarily considered as a factor of hydrostatic force acting laterally as UDL over the surface. Table 3 shows the magnitude of flood loads acting on the column for the frame models.


**Table 3.** Flood loads on frame models

**3.3. Analysis**

are the following:

**•** Running analysis. **•** Inferring the results.

**•** Modelling of frame sections.

**•** Assigning support conditions.

**•** Assigning load combinations. **•** Setting up of analysis option.

a) 1.5 (DL + IL) b) 1.2 (DL + IL ± EL) c) 1.5 (DL ± EL) d) 0.9 DL ± 1.5 EL

**3.4. Calculation of design moment**

**Table 2.** Zone factor (Ref. IS 1893-2002)

**3.5. Calculation of flood loads**

The procedure consists of linear static and linear dynamic analysis. When the linear static or dynamic procedures are used for seismic evaluation, the design seismic forces, the distribution of applied loads over the height of the buildings, and the corresponding displacements are determined using a linearly elastic analysis. The various steps involved in SAP model analysis

Analyses were carried out for six different conditions of seismic zones, flood duration, flood water height, flood forces, frame models, and support conditions, to obtain the maximum

The earthquake load calculations were made for all the zones and all the models analysed, and designed for IS 456:2000. Here, the earthquake zones are considered to demonstrate the different structural variations but not the multi-hazard conditions (Table 2). The design

Flood loads are assumed to act as: a) hydrostatic loads; b) impact loads as equivalent static loads; c) impact loads as dynamic loads, considering the duration of flood. The hydrostatic

**Seismic zone II III IV V** Seismic intensity Low Moderate Severe Very severe Z 0.1 0.16 0.24 0.36

**•** Defining and assigning material properties and section properties.

**•** Defining and assigning load patterns and load cases.

8 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

The load combinations considered for the study are:

design moment, flood moment and lateral displacements.

moment is lower for fixed support condition than hinged condition.

The flood loads are assumed as dynamic loads by considering the duration of flood td. The dynamic displacement and dynamic flood moment are found using a deformation response factor (R). R is the ratio dynamic to static displacement caused by the flood force. The dynamic flood load is assumed as a rectangular pulse (Fig.8.).

**Figure 8.** a) SDF system (b) Rectangular pulse load (Chopra, 2009)

The governing equation is:

$$m\ddot{\boldsymbol{u}} + k\boldsymbol{u} = p(t) = \begin{vmatrix} p\_0 & t \le t\_d \\ 0 & t \ge t\_d \end{vmatrix} \tag{1}$$

**•** Flood loadings: static, equivalent static, and dynamic loads;

For each zone, earthquake loads were assessed for all the zones and all the models designed for IS 456:2000. The earthquake zones (Table 5) are considered to demonstrate the different structural variations but not the multi-hazard conditions. The maxima design moments for both the bare frame and the frame with light weight partition walls are similar, since the weight of partition wall is considered as negligible. The sizes of frame sections, selected according to these moments, are given in Table 6. For the frame with structural infill, the infill walls were modelled as diagonal structures. After applying flood loads, for different frame models and in each zone, for hinged support condition, the maximum flood moment in each case was evaluated. Assuming flood heights of 2m, 3m and 4m from ground level, maxima moments were also obtained (Table 7). Because of the free movement of water in between the columns of the bare frame, the flood moment for bare frame model is very low if compared to the other

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**Zone Bare Frame Light weight infill Structural infill** II 33.56 33.56 64.90 III 45.00 45.00 92.66 IV 62.26 62.26 128.58 V 86.40 86.40 184.09

**hf (m) Bare Frame Light weight infill Structural infill** 5.74 32.14 30.09 9.45 97.58 83.94 20.18 205.84 166.33

**Table 7.** Flood moment due to hydrostatic force (without impact factor) in kN-m

**Frame model Column size Beam size** Bare Frame 300 x 300 250 x 300 Light weight infill 300 x 300 250 x 300 Structural infill 350 x 350 300 x 350

**•** Support conditions: hinged and fixed;

**•** Flood water height: 2m, 3m and 4m;

**Table 5.** Maximum design moments in kN-m

**Table 6.** Frame cross-sections in mm

**•** Flood duration: Tn/6, Tn/4, Tn/3 and Tn/2.

**•** Seismic zones;

models.

The R value obtained after solving the equation 2 is (Chopra 2009):

$$R = \frac{u}{u\_{st}} = \begin{cases} 2\sin\pi\tau \frac{t\_d}{T\_u} & \frac{t\_d}{T\_u} \le \frac{1}{2} \\\ 2 & \frac{t\_d}{T\_u} \ge \frac{1}{2} \end{cases} \tag{2}$$

Where, u is the dynamic displacement, ust is the static displacement, td is the flood duration and Tn is the fundamental natural time period of the structure. The td/Tn ratios and corre‐ sponding R values used are shown in Table 4. R = 1 indicates the flood as static while R = 2 indicates suddenly applied flood load. Since the flood assumed for the study is slow moving, R will always lies in between 1 and 2.


**Table 4.** Deformation response factor

#### **3.6. Calculation of flood moment and height**

Afterwards, analyses have to be carried out for different frame models in each zone with different boundary conditions, and the maximum flood moment in each case must be evalu‐ ated. The safe flood height is the height of flood up to which the structure is safe. It is obtained by plotting the moment due to hydrostatic force versus flood height: height corresponding to the design moment gives the safe flood height (hf , safe).

The vulnerability index is assessed as a factor of ground floor height. It indicates the extent of damage that a flood can cause if the water reaches up to ground floor height. It is calculated using the equation (3).

$$\text{Vulnerability index} = \frac{\text{ground\\_floor\\_height - safe\\_food\\_height}}{\text{ground\\_floor\\_height}} \tag{3}$$

#### **4. Experimental results**

The analysis was carried out for three frame models under different conditions of:


*mu*¨ <sup>+</sup> *ku* <sup>=</sup> *<sup>p</sup>*(*t*)={ *<sup>p</sup>*<sup>0</sup>

The R value obtained after solving the equation 2 is (Chopra 2009):

*ust* ={ <sup>2</sup> sin *<sup>π</sup>*

*<sup>R</sup>* <sup>=</sup> *<sup>u</sup>*

10 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

R will always lies in between 1 and 2.

**Table 4.** Deformation response factor

using the equation (3).

**4. Experimental results**

**3.6. Calculation of flood moment and height**

the design moment gives the safe flood height (hf

0

Where, u is the dynamic displacement, ust is the static displacement, td is the flood duration and Tn is the fundamental natural time period of the structure. The td/Tn ratios and corre‐ sponding R values used are shown in Table 4. R = 1 indicates the flood as static while R = 2 indicates suddenly applied flood load. Since the flood assumed for the study is slow moving,

**td/Tn** 1/6 1/4 1/3 1/2

**R** 1.000 1.4142 1.7321 2.000

Afterwards, analyses have to be carried out for different frame models in each zone with different boundary conditions, and the maximum flood moment in each case must be evalu‐ ated. The safe flood height is the height of flood up to which the structure is safe. It is obtained by plotting the moment due to hydrostatic force versus flood height: height corresponding to

The vulnerability index is assessed as a factor of ground floor height. It indicates the extent of damage that a flood can cause if the water reaches up to ground floor height. It is calculated

*Vulnerability index* <sup>=</sup> *ground floor height* - *safe flood height*

The analysis was carried out for three frame models under different conditions of:

, safe).

*ground floor height* (3)

*td Tn* 2

*t* ≤*td t* ≥*td*

*td Tn td Tn*

≤ ≥

(1)

(2)


For each zone, earthquake loads were assessed for all the zones and all the models designed for IS 456:2000. The earthquake zones (Table 5) are considered to demonstrate the different structural variations but not the multi-hazard conditions. The maxima design moments for both the bare frame and the frame with light weight partition walls are similar, since the weight of partition wall is considered as negligible. The sizes of frame sections, selected according to these moments, are given in Table 6. For the frame with structural infill, the infill walls were modelled as diagonal structures. After applying flood loads, for different frame models and in each zone, for hinged support condition, the maximum flood moment in each case was evaluated. Assuming flood heights of 2m, 3m and 4m from ground level, maxima moments were also obtained (Table 7). Because of the free movement of water in between the columns of the bare frame, the flood moment for bare frame model is very low if compared to the other models.


**Table 5.** Maximum design moments in kN-m


**Table 6.** Frame cross-sections in mm


**Table 7.** Flood moment due to hydrostatic force (without impact factor) in kN-m

Impact force is assumed to act as UDL, and its value is arbitrarily taken as a factor of hydrostatic force. The impact factors considered are 0.1 and 0.2. For all the models, the moments are linearly increasing as impact load increases, because impact force is considered as a factor of hydro‐ static load (Table 8). Non-linearity will come only while considering flood duration. Flood is assumed to act as dynamic rectangular load with flood duration td and the maximum flood moment obtained in each case is shown in Table 9.

**hf (m) Max flood moment (kN-m)**

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2 32.14 3 97.58 4 205.84

**Table 11.** Maximum flood moment for the frame with light weight partition wall in Zone II.

The vulnerability index of frame with light weight partition wall is high (49.3%) if compared to the other frames (Fig.8.). For frame with structural infill it only reaches a maximum of 32%, while it is zero for bare frame model. Vulnerability indexes obtained due to hydrostatic and equiva‐ lent static impact forces show that the highest values pertain to frame with light weight partition wall (Table 12). Vulnerability indexes obtained due to dynamic flood forces in various zones for

II 0 0 0.518 0.539 0.347 0.371 III 0 0 0.461 0.484 0.244 0.27 IV 0 0 0.391 0.416 0.134 0.163 V 0 0 0.311 0.339 0 0.027

**Bare Frame Light weight infill Structural infill 0.1γh<sup>f</sup> 0.2γh<sup>f</sup> 0.1γh<sup>f</sup> 0.2γh<sup>f</sup> 0.1γh<sup>f</sup> 0.2γh<sup>f</sup>**

**Figure 9.** Variation of vulnerability in various zones

different flood duration are shown in Table 13.

**Table 12.** Vulnerability due to hydrostatic and equivalent static impact forces

**Zone**


**Table 8.** Moment due to hydrostatic and equivalent static impact forces in kN-m


**Table 9.** Flood moment due to dynamic flood forces in kN-m


**Table 10.** Duration of flood (td) in sec

The fundamental frequency and duration of flood will be the same for both the frames. Also, the flood moment obtained is the same for frame with structural and non-structural partitions, be‐ cause the contact area of flood water is the same for both frames. The safe flood height is ob‐ tained by plotting the moment due to hydrostatic force versus flood height. For example, for a frame with light weight partition wall in Zone II, design moment is 33.56 kN-m (Table 5) and its maximum moment due to hydrostatic loading is shown in Table 11. From the graph, the safe flood height corresponding to design moment 33.5596 is 2.0276 m.


**Table 11.** Maximum flood moment for the frame with light weight partition wall in Zone II.

**Figure 9.** Variation of vulnerability in various zones

Impact force is assumed to act as UDL, and its value is arbitrarily taken as a factor of hydrostatic force. The impact factors considered are 0.1 and 0.2. For all the models, the moments are linearly increasing as impact load increases, because impact force is considered as a factor of hydro‐ static load (Table 8). Non-linearity will come only while considering flood duration. Flood is assumed to act as dynamic rectangular load with flood duration td and the maximum flood

> **Bare Frame Light weight infill Structural infill** 0.1γh<sup>f</sup> 0.2γh<sup>f</sup> 0.1γh<sup>f</sup> 0.2γh<sup>f</sup> 0.1γh<sup>f</sup> 0.2γh<sup>f</sup>

**Bare frame Frame with partitions R=1 R=1.4142 R=1.7321 R=2 R=1 R=1.4142 R=1.7321 R=2**

II 33.56 47.46 58.13 67.12 64.90 91.78 112.41 129.80 III 45.00 63.64 77.95 90.00 92.66 131.04 160.49 185.32 IV 62.26 88.05 107.84 124.52 128.58 181.84 222.71 257.16 V 86.40 122.18 149.65 172.80 184.09 260.35 318.87 368.19

**Frame type R=1 R=1.414 R=1.732 R=2** Bare frame and Frame with light weight infill 0.0448 0.0673 0.0897 0.1345 Frame with masonry infill 0.0092 0.0139 0.0185 0.0277

The fundamental frequency and duration of flood will be the same for both the frames. Also, the flood moment obtained is the same for frame with structural and non-structural partitions, be‐ cause the contact area of flood water is the same for both frames. The safe flood height is ob‐ tained by plotting the moment due to hydrostatic force versus flood height. For example, for a frame with light weight partition wall in Zone II, design moment is 33.56 kN-m (Table 5) and its maximum moment due to hydrostatic loading is shown in Table 11. From the graph, the safe

2 5.74 5.74 36.67 41.20 33.62 37.16 3 10.69 11.92 109.92 122.27 92.5228 101.10 4 22.53 24.89 229.40 252.96 180.05 193.77

moment obtained in each case is shown in Table 9.

12 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**Table 9.** Flood moment due to dynamic flood forces in kN-m

**Table 10.** Duration of flood (td) in sec

**Table 8.** Moment due to hydrostatic and equivalent static impact forces in kN-m

flood height corresponding to design moment 33.5596 is 2.0276 m.

**hf (m)**

**Zone**

The vulnerability index of frame with light weight partition wall is high (49.3%) if compared to the other frames (Fig.8.). For frame with structural infill it only reaches a maximum of 32%, while it is zero for bare frame model. Vulnerability indexes obtained due to hydrostatic and equiva‐ lent static impact forces show that the highest values pertain to frame with light weight partition wall (Table 12). Vulnerability indexes obtained due to dynamic flood forces in various zones for different flood duration are shown in Table 13.


**Table 12.** Vulnerability due to hydrostatic and equivalent static impact forces


**hf (m)**

**Zone**

**Bare Frame Light weight infill Structural infill 0.1γh<sup>f</sup> 0.2γh<sup>f</sup> 0.1γh<sup>f</sup> 0.2γh<sup>f</sup> 0.1γh<sup>f</sup> 0.2γh<sup>f</sup>**

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2 0.807 0.98 7.666 9.393 0.1 0.116

3 2.578 3.138 25.372 30.975 0.264 0.324

4 5.918 7.172 58.78 71.317 0.616 0.766

The relative cost for any frame model is calculated with respect to the design moment of bare

Where DMzoneIII,IV,V are the design moments in zones III, IV and V for frame with partitions,

The relative costs for the three frame models are shown in Table 16. The graph of relative cost versus vulnerability index shows that for the frame with light weight partition wall the cost is increasing but the vulnerability is not reducing that much. Moreover, even though the initial cost is higher for frame with structural partitions, its vulnerability is lower if compared to

II 33.560 0 33.560 0 64.898 1

III 45.001 0.341 45.001 0.341 92.658 1.761

IV 62.259 0.855 62.259 0.855 128.579 2.831

V 86.398 1.574 86.398 1.574 184.094 4.486

The vulnerability obtained for different flood loadings is compared with partitions, zones and flood duration (Fig.11. and 12). Dynamic load with R = 1.4142 is used for comparing the results

**Bare Frame Light weight infill Structural infill DM cost relative DM cost relative DM cost relative**

*DM zone II* (*bare*)

(4)

15

*Cost relative zone III* ,*IV* ,*<sup>V</sup>* <sup>=</sup> *DM zone III* ,*IV* ,*<sup>V</sup>* - *DM zone II* (*bare*)

and DMzoneII(bare) is the design bending moment of bare frame in zone II.

**Table 15.** Storey drifts due to hydrostatic and equivalent static forces

frame with non-structural partitions (Fig.10.).

**Table 16.** Relative cost as a factor of design moment for three frame models

frame model in zone II (Eq. 4):

**Table 13.** Vulnerability index due to dynamic flood forces

The storey drifts are evaluated from the lateral joint displacements. According to IS 1893-2002 Cl.7.11.1, the maximum storey drift is 0.004 H, where H is the height of the building. In this study, H = 13 m and hence the maximum allowable storey drift is 52 mm. The frame with structural infill wall has low storey drift if compared to bare frame, because infill walls have significant effect in resisting lateral storey drift (Table 14). For the frame with light weight partition wall, storey drift reaches 71.32mm, which is more than that specified for seismic resistant building (Table 15). Hence a frame with non-structural partitions with hinged support is not preferred in flood prone areas.


**Table 14.** Storey drifts due to hydrostatic forces


**Table 15.** Storey drifts due to hydrostatic and equivalent static forces

**Bare frame** R 1 1.414 1.732 2 Zone II 0 0.142 0.300 0.393 Zone III 0 0.061 0.233 0.336 Zone IV 0 0.000 0.160 0.272 Zone V 0 0.000 0.085 0.208 **Frame with light weight infill** Zone II 0.493 0.642 0.707 0.747 Zone III 0.435 0.600 0.674 0.717 Zone IV 0.362 0.549 0.632 0.681 Zone V 0.279 0.490 0.584 0.639 **Frame with structural infill** Zone II 0.320 0.519 0.607 0.660 Zone III 0.214 0.444 0.546 0.607 Zone IV 0.103 0.365 0.482 0.551 Zone V 0.000 0.266 0.401 0.481

14 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

The storey drifts are evaluated from the lateral joint displacements. According to IS 1893-2002 Cl.7.11.1, the maximum storey drift is 0.004 H, where H is the height of the building. In this study, H = 13 m and hence the maximum allowable storey drift is 52 mm. The frame with structural infill wall has low storey drift if compared to bare frame, because infill walls have significant effect in resisting lateral storey drift (Table 14). For the frame with light weight partition wall, storey drift reaches 71.32mm, which is more than that specified for seismic resistant building (Table 15). Hence a frame with non-structural partitions with hinged support

**hf (m) Bare Frame Light weight infill Structural infill** 0.634 5.939 0.084 2.017 19.696 0.205 4.665 46.242 0.466

**Table 13.** Vulnerability index due to dynamic flood forces

is not preferred in flood prone areas.

**Table 14.** Storey drifts due to hydrostatic forces

The relative cost for any frame model is calculated with respect to the design moment of bare frame model in zone II (Eq. 4):

$$\text{Cost\\_relative}\_{zone\\_III,IV,V} = \frac{\text{DM}\_{zone\\_III,IV,V} \cdot \text{DM}\_{zone\\_II\\_lor}}{\text{DM}\_{zone\\_II\\_lor}} \tag{4}$$

Where DMzoneIII,IV,V are the design moments in zones III, IV and V for frame with partitions, and DMzoneII(bare) is the design bending moment of bare frame in zone II.

The relative costs for the three frame models are shown in Table 16. The graph of relative cost versus vulnerability index shows that for the frame with light weight partition wall the cost is increasing but the vulnerability is not reducing that much. Moreover, even though the initial cost is higher for frame with structural partitions, its vulnerability is lower if compared to frame with non-structural partitions (Fig.10.).


**Table 16.** Relative cost as a factor of design moment for three frame models

The vulnerability obtained for different flood loadings is compared with partitions, zones and flood duration (Fig.11. and 12). Dynamic load with R = 1.4142 is used for comparing the results with static results. Frame with light weight infill wall is more vulnerable (64.2%) and bare frame is less vulnerable (14.2%). This is due to the free movement of water in between the columns of the bare frame, so that the contact area of flood water is very low if compared to the other frames. For the frame with masonry infill, vulnerability is less compared to light weight partition, even though the flood moment is the same for both the cases. It is due to the structural action of masonry infill against the lateral flood load.

Comparing vulnerability for different flood loadings to seismic zones (Fig.13.), for the frame with light weight infill, vulnerability is higher in Zone II (64.2%) and it reduces as zone increases (zone V: 49%). For frame with masonry infill, vulnerability is reaching zero as zone varies from II (51.9%) to V (Fig.14.). This is because the design moment of building in zone V is higher if compared to zone II and hence the building in zone V will be more resistive to flood.

**Figure 11.** Vulnerability for different frame models in different flood loading conditions in Zone II

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**Figure 12.** Vulnerability for different frame models in different flood loading conditions in Zone V

**Figure 11.** Vulnerability for different frame models in different flood loading conditions in Zone II

with static results. Frame with light weight infill wall is more vulnerable (64.2%) and bare frame is less vulnerable (14.2%). This is due to the free movement of water in between the columns of the bare frame, so that the contact area of flood water is very low if compared to the other frames. For the frame with masonry infill, vulnerability is less compared to light weight partition, even though the flood moment is the same for both the cases. It is due to the

Comparing vulnerability for different flood loadings to seismic zones (Fig.13.), for the frame with light weight infill, vulnerability is higher in Zone II (64.2%) and it reduces as zone increases (zone V: 49%). For frame with masonry infill, vulnerability is reaching zero as zone varies from II (51.9%) to V (Fig.14.). This is because the design moment of building in zone V is higher if compared to zone II and hence the building in zone V will be more resistive to

structural action of masonry infill against the lateral flood load.

16 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

flood.

**Figure 10.** Variation of vulnerability against cost

**Figure 12.** Vulnerability for different frame models in different flood loading conditions in Zone V

**Figure 13.** Vulnerability for light weight infill frame under different flood loading conditions in different zones

**Figure 15.** Vulnerability for different frame models under different flood duration in Zone II

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**Figure 16.** Vulnerability for the frame with light weight infill in different zones

**Figure 14.** Vulnerability masonry infill frame under different flood loading conditions in different zones

**Figure 15.** Vulnerability for different frame models under different flood duration in Zone II

**Figure 13.** Vulnerability for light weight infill frame under different flood loading conditions in different zones

18 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**Figure 14.** Vulnerability masonry infill frame under different flood loading conditions in different zones

**Figure 16.** Vulnerability for the frame with light weight infill in different zones

Analysing different frame models under different flood duration in Zone II (Fig.15.), vulner‐ ability increases with the duration of flood, but it is lower for bare frame (39.3%) if compared to frame with partitions (74.7% for light weight infill and 66% for frame with structural infill). It is due to the free movement of flood water between the columns of bare frame. The results of vulnerability for the frame with light weight infill (Fig. 16.), show that a building in zone V with flood duration Tn/3 is less vulnerable (58.4%) than a building in zone II with flood duration of Tn/2 (64.2%).

The analysis was carried out for all the cases, keeping the support of columns as fixed. The earthquake load calculations were made for all the zones and all the models analysed and designed as per IS 456:2000, for each zone and maximum design moments (Table 17). The maximum moment is lower for the fixed support condition, so the cross sections required is lower when compared to hinge support condition. The sizes of frame sections are given in Table 18. Fig. 18. shows the variation of flood moments for different frame models due to hydrostatic force. The flood moments parabolically increase as flood water height increases.


shown in Table 21. Vulnerability for the bare frame is zero in all the seismic zones but it is nonzero for frame with partitions (Fig. 17.). This is due to the free movement of water in between the columns of the bare frame, so that the contact area of flood water will be low if compared the other frames. The vulnerability of frame with light weight partition wall is very high (60.3%), while for frame with structural infill it reaches 44.6% and it is not present for bare

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**Figure 17.** Variation of flood moment to hydrostatic force (without impact factor) with water height

Vulnerability indexes obtained due to hydrostatic and dynamic impact forces for fixed support

2 4.4614 4.462 35.7516 42.4092 31.2512 36.9778

3 9.6472 11.2901 102.16 118.589 87.7618 100.1991

4 19.6866 22.5464 202.5541 231.1526 164.8106 182.9759

**Bare Frame Light weight infill Structural infill 0.1γhf 0.2γhf 0.1γhf 0.2γhf 0.1γhf 0.2γhf**

condition are shown in Table 22 and 23, respectively.

**Table 19.** Moment due to hydrostatic and impact forces in kN-m

frame model.

**hf (m)**

**Table 17.** Maximum design moment in kN-m


**Table 18.** Frame cross-sections in mm

The maximum moments obtained from the analysis for fixed support condition are shown in Table 19. For all frame models, the moments linearly increase as impact load increases. This is because, for the present case, impact force is considered as factor of hydrostatic load. Nonlinearity will come only while considering flood duration. The duration of flood load (td) considered for various R values for hinged support condition are shown in Table 20 and the flood moments due to dynamic flood loads in various zones for fixed support condition are

**Figure 17.** Variation of flood moment to hydrostatic force (without impact factor) with water height

shown in Table 21. Vulnerability for the bare frame is zero in all the seismic zones but it is nonzero for frame with partitions (Fig. 17.). This is due to the free movement of water in between the columns of the bare frame, so that the contact area of flood water will be low if compared the other frames. The vulnerability of frame with light weight partition wall is very high (60.3%), while for frame with structural infill it reaches 44.6% and it is not present for bare frame model.

Vulnerability indexes obtained due to hydrostatic and dynamic impact forces for fixed support condition are shown in Table 22 and 23, respectively.


**Table 19.** Moment due to hydrostatic and impact forces in kN-m

Analysing different frame models under different flood duration in Zone II (Fig.15.), vulner‐ ability increases with the duration of flood, but it is lower for bare frame (39.3%) if compared to frame with partitions (74.7% for light weight infill and 66% for frame with structural infill). It is due to the free movement of flood water between the columns of bare frame. The results of vulnerability for the frame with light weight infill (Fig. 16.), show that a building in zone V with flood duration Tn/3 is less vulnerable (58.4%) than a building in zone II with flood duration

20 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

The analysis was carried out for all the cases, keeping the support of columns as fixed. The earthquake load calculations were made for all the zones and all the models analysed and designed as per IS 456:2000, for each zone and maximum design moments (Table 17). The maximum moment is lower for the fixed support condition, so the cross sections required is lower when compared to hinge support condition. The sizes of frame sections are given in Table 18. Fig. 18. shows the variation of flood moments for different frame models due to hydrostatic force. The flood moments parabolically increase as flood water height increases.

**Zone Bare Frame Light weight infill Structural infill** II 16.1389 16.1389 33.6639

III 25.3319 25.3319 49.3

IV 30.6598 30.6598 69.5349

V 42.6198 42.6198 100.8072

**Frame model Column size Beam size** Bare Frame 250 x 250 250 x 300

Light wt infill 250 x 250 250 x 300

Structural infill 300 x 300 250 x 300

The maximum moments obtained from the analysis for fixed support condition are shown in Table 19. For all frame models, the moments linearly increase as impact load increases. This is because, for the present case, impact force is considered as factor of hydrostatic load. Nonlinearity will come only while considering flood duration. The duration of flood load (td) considered for various R values for hinged support condition are shown in Table 20 and the flood moments due to dynamic flood loads in various zones for fixed support condition are

of Tn/2 (64.2%).

**Table 17.** Maximum design moment in kN-m

**Table 18.** Frame cross-sections in mm


**Zone**

**Bare Frame Light weight infill Structural infill 0.1γh<sup>f</sup> 0.2γh<sup>f</sup> 0.1γh<sup>f</sup> 0.2γh<sup>f</sup> 0.1γh<sup>f</sup> 0.2γh<sup>f</sup>**

Vulnerability of Reinforced Concrete Structures Subjected to Flood

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23

II 0 0 0.638 0.663 0.488 0.523 III 0 0 0.566 0.596 0.400 0.438 IV 0 0 0.531 0.564 0.308 0.348 V 0 0 0.466 0.501 0.193 0.235

**Bare frame R 1 1.414 1.732 2** Zone II 0.000 0.298 0.427 0.503 Zone III 0.000 0.178 0.329 0.419 Zone IV 0.000 0.128 0.288 0.383 Zone V 0.000 0.040 0.217 0.321 **Frame with light weight infill** Zone II 0.603 0.719 0.771 0.802 Zone III 0.528 0.666 0.727 0.764 Zone IV 0.491 0.640 0.706 0.746 Zone V 0.422 0.592 0.667 0.711 **Frame with structural infill** Zone II 0.445 0.608 0.680 0.723 Zone III 0.355 0.544 0.628 0.678 Zone IV 0.262 0.478 0.574 0.631 Zone V 0.145 0.396 0.507 0.573

The storey drift is lower for fixed support condition and the maximum value concerns the frame with light weight partition walls (Fig. 19.). The frame with structural infill wall show the smallest storey drift: this indicates the significance of infill in resisting lateral storey drift. Storey drift reaches the maximum of 20.188 mm for the frame with light weight partition walls,

For the frame with structural infill wall, even though the initial relative cost is high, the vul‐ nerability is lower if compared to frame with non-structural partition walls (Table 25).

which is less than that specified for seismic resistant building (Table 24).

**Table 22.** Vulnerability index due to hydrostatic and impact forces

**Table 23.** Vulnerability index due to dynamic flood forces

**Table 20.** Duration of flood (td) in sec


**Figure 18.** Variation of vulnerability in various zones


**Table 22.** Vulnerability index due to hydrostatic and impact forces

**Frame type R=1 R=1.414 R=1.732 R=2** Bare frame 0.0401 0.0601 0.0801 0.1202

**Bare frame Frame with structural infill**

**R=1 R=1.4142 R=1.7321 R=2 R=1 R=1.4142 R=1.7321 R=2**

II 16.14 22.82 27.95 32.28 33.66 47.61 58.31 67.33 III 25.33 35.82 43.88 50.66 49.30 69.72 85.39 98.60 IV 30.66 43.36 53.11 61.32 69.53 98.34 120.44 139.07 V 42.62 60.27 73.82 85.24 100.81 142.56 174.61 201.61

Frame with masonry infill 0.0098 0.0147 0.0196 0.0294

**Table 20.** Duration of flood (td) in sec

**Table 21.** Flood moment due to dynamic flood forces in kN-m

22 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**Figure 18.** Variation of vulnerability in various zones

**Zone**


**Table 23.** Vulnerability index due to dynamic flood forces

The storey drift is lower for fixed support condition and the maximum value concerns the frame with light weight partition walls (Fig. 19.). The frame with structural infill wall show the smallest storey drift: this indicates the significance of infill in resisting lateral storey drift. Storey drift reaches the maximum of 20.188 mm for the frame with light weight partition walls, which is less than that specified for seismic resistant building (Table 24).

For the frame with structural infill wall, even though the initial relative cost is high, the vul‐ nerability is lower if compared to frame with non-structural partition walls (Table 25).

**Figure 19.** Variation of storey drift with flood water height


The vulnerability results obtained for different flood loadings are compared with respect to partitions (Fig. 20. and 21.). The frame with light weight infill wall is more vulnerable and bare frame is least vulnerable. This is due to the free movement of water in between the columns of the bare frame so that the contact area of flood water is lower if compared to the other frames. For the frame with masonry infill, vulnerability is lower if compared to light weight partition, even though the flood moment is the same for both the cases (Fig. 20.). It is due to the structural

Vulnerability of Reinforced Concrete Structures Subjected to Flood

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25

**Figure 20.** Vulnerability for different frame models under different flood loading conditions in Zone V

The vulnerability reduces from zone II to zone V because the design moment in zone V is higher if compared to zone II and hence the building is more resistive to flood. The variation of vulnerability for the frame with light weight infill and with masonry infill under different

For the frame with light weight infill, vulnerability is higher in Zone II (71.9%) and it reduced as zone increases (Fig. 22.). For frame with masonry infill, vulnerability is higher in Zone II (60.8%) and it decreases as zone increases (Fig. 23.). This is because the design moment of building in zone V is higher if compared to zone II and hence the building in zone V will be

The vulnerability results obtained for different flood loadings are compared with respect to seismic zones (Fig. 24. and 25.). As the duration of flood increases, vulnerability increases (Fig. 24.); vulnerability is lower for bare frame than for frame with partitions. A building in zone V

flood loading conditions in different zones are shown in Fig. 22. and 23, respectively.

action of masonry infill against the lateral flood load.

more resistant to flood.

**Table 24.** Storey drifts due to hydrostatic and impact forces in mm


**Table 25.** Relative cost as a factor of design moment for three frame models

**Figure 20.** Vulnerability for different frame models under different flood loading conditions in Zone V

**Figure 19.** Variation of storey drift with flood water height

24 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**Table 24.** Storey drifts due to hydrostatic and impact forces in mm

**Table 25.** Relative cost as a factor of design moment for three frame models

**Bare Frame Light weight infill Structural infill 0.1γh<sup>f</sup> 0.2γh<sup>f</sup> 0.1γh<sup>f</sup> 0.2γh<sup>f</sup> 0.1γh<sup>f</sup> 0.2γh<sup>f</sup>**

2 0.18 0.22 1.24 1.64 0.09 0.103 3 0.605 0.778 5.497 7.229 0.22 0.274 4 1.618 2.074 15.621 20.188 0.534 0.68

**Bare Frame Light weight infill Structural infill DM cost relative DM cost relative DM cost relative**

II 16.139 0.000 16.139 0.000 33.664 1.086 III 25.332 0.570 25.332 0.570 49.300 2.055 IV 30.660 0.900 30.660 0.900 69.535 3.309 V 42.620 1.641 42.620 1.641 100.807 5.246

**hf (m)**

**Zone**

The vulnerability results obtained for different flood loadings are compared with respect to partitions (Fig. 20. and 21.). The frame with light weight infill wall is more vulnerable and bare frame is least vulnerable. This is due to the free movement of water in between the columns of the bare frame so that the contact area of flood water is lower if compared to the other frames. For the frame with masonry infill, vulnerability is lower if compared to light weight partition, even though the flood moment is the same for both the cases (Fig. 20.). It is due to the structural action of masonry infill against the lateral flood load.

The vulnerability reduces from zone II to zone V because the design moment in zone V is higher if compared to zone II and hence the building is more resistive to flood. The variation of vulnerability for the frame with light weight infill and with masonry infill under different flood loading conditions in different zones are shown in Fig. 22. and 23, respectively.

For the frame with light weight infill, vulnerability is higher in Zone II (71.9%) and it reduced as zone increases (Fig. 22.). For frame with masonry infill, vulnerability is higher in Zone II (60.8%) and it decreases as zone increases (Fig. 23.). This is because the design moment of building in zone V is higher if compared to zone II and hence the building in zone V will be more resistant to flood.

The vulnerability results obtained for different flood loadings are compared with respect to seismic zones (Fig. 24. and 25.). As the duration of flood increases, vulnerability increases (Fig. 24.); vulnerability is lower for bare frame than for frame with partitions. A building in zone V

**Figure 21.** Vulnerability for different frame models under different flood loading conditions in Zone II

with flood duration Tn/3 is less vulnerable (66.7%) than a building in zone II with flood duration of Tn/2 (71.9%) (Fig. 25.), hence vulnerability is higher for building subjected to longer floods

**Figure 23.** Vulnerability for the frame with masonry infill under different flood loading conditions in different zones

Vulnerability of Reinforced Concrete Structures Subjected to Flood

http://dx.doi.org/10.5772/53879

27

**Figure 24.** Vulnerability for different frame models under different flood duration in Zone II

even if it also depends on the seismic zone.

**Figure 22.** Vulnerability for the frame with light weight infill under different flood loading conditions in different zones

**Figure 23.** Vulnerability for the frame with masonry infill under different flood loading conditions in different zones

**Figure 21.** Vulnerability for different frame models under different flood loading conditions in Zone II

26 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**Figure 22.** Vulnerability for the frame with light weight infill under different flood loading conditions in different

zones

with flood duration Tn/3 is less vulnerable (66.7%) than a building in zone II with flood duration of Tn/2 (71.9%) (Fig. 25.), hence vulnerability is higher for building subjected to longer floods even if it also depends on the seismic zone.

**Figure 24.** Vulnerability for different frame models under different flood duration in Zone II

**•** Storey drift for frame with light weight partition wall in fixed support condition is found to be less than hinged condition. The maximum value of storey drift for frame with light

Vulnerability of Reinforced Concrete Structures Subjected to Flood

http://dx.doi.org/10.5772/53879

29

**•** Even though the initial cost is more for frame with structural partitions, its vulnerability is

**•** Buildings in zone II is most vulnerable and the vulnerability is reducing as zone increases. It reaches zero for frame with structural infill ass zone varies from zone II to zone V. This is because the design moment of building in zone V is higher if compared to zone II and hence

Frame with light weight partition wall result as the most vulnerable and bare frame is least vulnerable. Hence frame with non-structural partitions like plywood are not preferred in flood prone areas. The storey drift for the frame with structural infill walls is very low if compared to the other frame models and this indicates the significance of infill in resisting lateral storey drift. Soft storied buildings are less vulnerable compared to ordinary buildings and this depends on the free movement of water in between the columns. Results also indicate the real need of considering the flood loads in the design procedure of reinforced concrete buildings.

and Remya Retnan

Department of Civil Engineering, National Institute of Technology Tiruchirappalli, India

[1] American Society of Civil Engineers (2006), Minimum Design Loads for Buildings

[2] Chopra, A.K. Dynamic of Structures - Theory and Applications to Earthquake Engi‐

[3] Arulselvan, S., Subramanian K., Pillai E. B.P., and Santhakumar A. R.(2007), RC Infil‐ led frames - RC Plane Frame Interactions for Seismic Resistance, *Journal of Applied*

[4] Federal Emergency Management Agency (2001), Engineering Principles and Practi‐

ces for Flood Prone Residential Structures, *FEMA 259*, Edition 2.

very low if compared to frame with non-structural partitions.

the building in zone V is more resistive to flood.

weight partition wall is 20.188mm.

**Author details**

**References**

Natarajan Chidambarathanu\*

*Sciences*, 7, 942-950.

\*Address all correspondence to: nataraj@nitt.edu

and Other Structures, ASCEI/SEI 7-05.

neering, Third Edition, Pearson Education, 2009.

**Figure 25.** Vulnerability for the frame with light weight infill in different zones

#### **5. Conclusions**

Flood physical vulnerability deals with the level of loss that elements at risk or built environ‐ ment suffer from the occurrence of flooding. This study aims to find out the flood vulnerability limit as a factor of ground floor height under flood forces and to quantify flood load. Three frame models were modelled and the effects of flood forces in each frame were analysed. The significance of infill walls in resisting lateral storey drift during flood is also investigated. The main conclusions of the analysis are:


Frame with light weight partition wall result as the most vulnerable and bare frame is least vulnerable. Hence frame with non-structural partitions like plywood are not preferred in flood prone areas. The storey drift for the frame with structural infill walls is very low if compared to the other frame models and this indicates the significance of infill in resisting lateral storey drift. Soft storied buildings are less vulnerable compared to ordinary buildings and this depends on the free movement of water in between the columns. Results also indicate the real need of considering the flood loads in the design procedure of reinforced concrete buildings.

#### **Author details**

**Figure 25.** Vulnerability for the frame with light weight infill in different zones

28 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

Flood physical vulnerability deals with the level of loss that elements at risk or built environ‐ ment suffer from the occurrence of flooding. This study aims to find out the flood vulnerability limit as a factor of ground floor height under flood forces and to quantify flood load. Three frame models were modelled and the effects of flood forces in each frame were analysed. The significance of infill walls in resisting lateral storey drift during flood is also investigated. The

**•** The flood moments parabolically increase as flood water height increases and linearly

**•** The vulnerability of frame with light weight partition wall, for hinged support condition, reaches 64.2% for dynamic flood forces, that is very high if compared to the other frames.

**•** For frame with light weight partition wall in hinged support condition, storey drift reaches

**•** The vulnerability of frame with light weight partition wall, for fixed support condition, is

71.32 mm, which is more than the value specified for seismic resistant building.

up to 60% in zone II which is very high if compared to the other frames.

**5. Conclusions**

main conclusions of the analysis are:

increase as impact load increases.

Natarajan Chidambarathanu\* and Remya Retnan

\*Address all correspondence to: nataraj@nitt.edu

Department of Civil Engineering, National Institute of Technology Tiruchirappalli, India

#### **References**


[5] Haugen E.D., and Kaynia A.M. (2008), Vulnerability of structures impacted by debris flow, *Landslides and Engineered Slopes*, Taylor & Francis Group, London, ISBN 978-0-415-41196-7, 381-387.

**Chapter 2**

**Multi-Tier Networks for Citywide Damage Monitoring in**

Progress of computer networks and mobile communications are leading to the environments capable of accessing networks anytime, anywhere. Furthermore, ubiquitous networks which are emerging in a smart city would awaken expectations to acquire any information with a hotspot panel in a whole city [1]. People expect to acquire information through the Internet with mobile devices or information appliances as usual, even in case of a large scale natural disaster. They also expect to contact with family and friends by mobile phones anytime. Though quick, accurate damage informationhas beenstrongly required for speedy and effective rescue operation, those communication systems did not work sufficientlyinthe previous large-scale disasters, due to both damage on facilitiesand communications congestion by heavy use or network overload [2, 3]. As a result, response efforts were delayed, causing

To solve the issue on collecting damage information and personal safety information in a natural disaster, several studieshave been carried out,for example, to provide emergency services in Internet [4] and to maintain communications in evacuation shelters [5]. The Journal of IEICE introduced policy for acquiring damage information and maintaining communica‐ tions in a disaster [6]. Moreover, regarding recovery from disaster damage in networks, telecommunication service companies have endeavored to mitigate aftermath of a disaster effectively [7]. However, in case of the Great East Japan Earthquake in 2011, telecommunication systems and networks could not maintained services after all [8, 9]. As a result, authorities could not comprehend the damage situations quickly, due to not only the scale of disaster-

This paper firstly reviews some networking technologies for disaster communications, and discusses a scheme on multi-tier damage monitoring in a citywide area. Then, an experimental

and reproduction in any medium, provided the original work is properly cited.

© 2013 Fujiwara and Watanabe; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

further damage that could have been prevented with better communications.

affected area but also to the loss of lines of communications [10].

**a Natural Disaster**

http://dx.doi.org/10.5772/55513

**1. Introduction**

Takahiro Fujiwara and Takashi Watanabe

Additional information is available at the end of the chapter


## **Multi-Tier Networks for Citywide Damage Monitoring in a Natural Disaster**

Takahiro Fujiwara and Takashi Watanabe

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55513

### **1. Introduction**

[5] Haugen E.D., and Kaynia A.M. (2008), Vulnerability of structures impacted by debris flow, *Landslides and Engineered Slopes*, Taylor & Francis Group, London, ISBN

[6] Kelman, I. (2002), Physical Flood Vulnerability of Residential Properties in Coastal,

[7] Kelman, I., and Spenc, R. (2004), An overview of flood actions on buildings, *Journal of*

[8] IS 1893 (Part 1): 2002, Indian Standard Criteria for Earthquake Resistant Design of

[10] IS: 875 (Part 1): 1987, Code of practice for design loads (other than earthquake) for buildings and structures: Part-1 Dead loads - unit weights of building materials and

[11] IS: 875 (Part 2): 1987, Code of practice for design loads (other than earthquake) for

[12] Kreibich H., Piroth K., Seifert I., Maiwald H., Kunert U., Schwarz J., Merz B., and Thieken, A. H. (2009), Is flow velocity a significant parameter in flood damage mod‐

[13] Messener, F., and Meyer, V. (2005), Flood Damage, Vulnerability and Risk Perception – Challenges for Flood Damage Research, Discussion Papers, Nato Science Series,

[14] Sagala, S.A.H. (2006), Analysis of flood physical vulnerability in residential areas, *M.Sc. Thesis*, International Institute of Geo-Information Science and Earth Observa‐

[15] Scheuren, J. M., de Waroux, O., Below, R., Guha-Saphir, D. and Ponserre, S. (2007),

[16] Schwarz, J. and Maiwald, H. (2008), Damage and loss prediction model based on the vulnerability of building types, *4th International Symposium on Flood Defence: Manag‐*

[18] Singh, A. K., and Sharma, A. K. (2009). GIS and a remote sensing based approach for urban floodplain mapping for the Tapi catchment, India. Hydro informatics in Hy‐ drology, Hydrogeology and Water Resources at the Joint IAHS & IAH Convention,

Revision, BIS, New Delhi.

Revision, BIS, New Delhi.

Revision, BIS, New

Edition, BIS, New

Eastern England, Ph.D. Dissertation, University of Cambridge, U.K.

Structures: Part-1 General Provisions and Buildings, 5th

buildings and structures: Part-2 Imposed Loads, 2th

elling?, *Natural Hazards Earth System Sciences*, 9, 1679–1692.

Annual Disaster Statistical Review. CRED Brussels, Belgium.

[17] SP-16: 1980, Design aids for reinforced concrete to IS: 456-1978, 11th

*ing Flood Risk, Reliability & Vulnerability*, May 6-8.

Hyderabad, India, September 2009.

[9] IS: 456: 2000 Plain and Reinforced Concrete -Code of Practice, 4th

Revision, BIS, New Delhi.

978-0-415-41196-7, 381-387.

30 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

*Engineering Geology*, 73, 297–309.

Delhi.

stored materials, 2th

Springer Publisher.

tion, Netherlands.

Delhi.

Progress of computer networks and mobile communications are leading to the environments capable of accessing networks anytime, anywhere. Furthermore, ubiquitous networks which are emerging in a smart city would awaken expectations to acquire any information with a hotspot panel in a whole city [1]. People expect to acquire information through the Internet with mobile devices or information appliances as usual, even in case of a large scale natural disaster. They also expect to contact with family and friends by mobile phones anytime. Though quick, accurate damage informationhas beenstrongly required for speedy and effective rescue operation, those communication systems did not work sufficientlyinthe previous large-scale disasters, due to both damage on facilitiesand communications congestion by heavy use or network overload [2, 3]. As a result, response efforts were delayed, causing further damage that could have been prevented with better communications.

To solve the issue on collecting damage information and personal safety information in a natural disaster, several studieshave been carried out,for example, to provide emergency services in Internet [4] and to maintain communications in evacuation shelters [5]. The Journal of IEICE introduced policy for acquiring damage information and maintaining communica‐ tions in a disaster [6]. Moreover, regarding recovery from disaster damage in networks, telecommunication service companies have endeavored to mitigate aftermath of a disaster effectively [7]. However, in case of the Great East Japan Earthquake in 2011, telecommunication systems and networks could not maintained services after all [8, 9]. As a result, authorities could not comprehend the damage situations quickly, due to not only the scale of disasteraffected area but also to the loss of lines of communications [10].

This paper firstly reviews some networking technologies for disaster communications, and discusses a scheme on multi-tier damage monitoring in a citywide area. Then, an experimental

© 2013 Fujiwara and Watanabe; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

system configured with a centralized hierarchical network, which was developed to acquire damage information from lifeline facilities installed in residences is shown. Finally, some results of computer simulation for multi-tier networks enhanced with an *ad hoc* networking technique are also presented.

**2.2. Cellular networks in a disaster**

connectivity even if difficult.

**2.3. Ad hoc networks**

The third-generation (3G) mobile systemshave provided the performance of up to 2 Mbps and various services in application systems [11]. Furthermore, current LTE (Long Term Evolution) and LTE-Advanced systems are providing a broadband mobile communications, leading to the fourth-generation (4G) cellular system, whichshould operate at the data rate of 100 Mbps or more [12, 13]. Since the latest mobile networks are challenging to provide a high data rate and a high capacity, those systems are required to operate at a higher carrier frequency and a large peak transmission power. A concept of virtual cellular system has been studiedand achieved to reduce the average transmission power compared with conventional cellular systems[14]. Thus, the current mobile systems have been developed focusing on high data rate and high capacity, under the policy of the best effort performance in ordinary conditions. However, in a disaster, it is strongly required for the communication systems to ensure

Multi-Tier Networks for Citywide Damage Monitoring in a Natural Disaster

http://dx.doi.org/10.5772/55513

33

In the past,immediately after a large-scale natural disaster, massive access to communications systems occurred, and the systems lapsed into communications congestion, in the worst cases resulting in system failure. To both mitigate the congestion and prevent system failure, accessibilityingeneral channels for citizensis restricted into 1/n(Fig. 2). Since the regulation is mainly applied to the telephone call, data communications such as e-mail services might be maintained, even if taking a long delivery time. Meanwhile, prioritized channels have been set up in advance, to maintain connection in a disaster, but the number of the channels is not

Multi-hopping is one solution to extend and maintain the coverage. A technical report of 3GPP (Third Generation Partnership Project) showeda scheme called ODMA (Opportunity Driven Multiple Access) to maintain high data rate in the edges of the coverage by relaying commu‐ nications (Fig.3)[15]. Mobile stations located in the high data-rate can access the Base Station (BS) directly. On the other hand, stations outside of the high data-rate cannot maintain the rate.

Similar idea was proposed, referred to as MRAC (Multi-hop Radio Access Cellular), which aims high speed, high capacity and wide area coverage by multi-hopping [16]. In the system, dedicated Repeater Stations (RS) are set up in a good propagation area, and the stations relay packets between user terminals and BS. In the event that a mobile station detects high propagation loss in the single-hop conditions to BS, the station selects a neighboring RS to relay packets. Hereby, MRAC is capable of expanding the coverage. However, since MRAC premisesmulti-hoping via RS, the restriction of the arrangement of RS reduces flexibility of the

Ad hoc network is a scheme to flexibly build a network without infrastructure facilities [17]. The networkis expected to maintain communications and to collect information even in a disaster. Figure 4 shows a model ofad hoc networks, where terminals are deployed and connected each other flexibly with wireless communications. For example, in rescue opera‐

They request a terminal located in the high data-rate area to relay their packets.

system operation. One solution is *ad hoc* networking, to build a network flexibly.

enough to transmit information from a large-scale damaged areas.

#### **2. Technologies for disaster communications**

This section reviews some technologies that should be effective for disaster communications to acquire damage information in a large-scale natural or manmade disaster, including related studies on disaster communications.

#### **2.1. A concept on damage monitoring in micro and micro perspectives**

A concept of an integrated damage monitoring and assessment system was proposed [3], referred to as macro and micro perspectives (Fig. 1). The macro perspective performs com‐ prehensive damage detection using image processing technique with satellite or aerial image. In addition, damage estimation in the aftermath of a disaster should be included in the perspective. The micro perspective, on the other hand, gathers individual damage information from a local site using several sensing devices, receiving emergency calls from suffers, and sharing information about rescue operations. Thus, the damage monitoring system needs to handle several types of information based on macro and micro perspectives. Multi-tier networks described in this paper play a critical role for the micro perspective.

**Figure 1.** A concept of damage monitoring and assessment in a large-scale natural disaster based on macro and micro perspectives

#### **2.2. Cellular networks in a disaster**

system configured with a centralized hierarchical network, which was developed to acquire damage information from lifeline facilities installed in residences is shown. Finally, some results of computer simulation for multi-tier networks enhanced with an *ad hoc* networking

This section reviews some technologies that should be effective for disaster communications to acquire damage information in a large-scale natural or manmade disaster, including related

A concept of an integrated damage monitoring and assessment system was proposed [3], referred to as macro and micro perspectives (Fig. 1). The macro perspective performs com‐ prehensive damage detection using image processing technique with satellite or aerial image. In addition, damage estimation in the aftermath of a disaster should be included in the perspective. The micro perspective, on the other hand, gathers individual damage information from a local site using several sensing devices, receiving emergency calls from suffers, and sharing information about rescue operations. Thus, the damage monitoring system needs to handle several types of information based on macro and micro perspectives. Multi-tier

> Integrated Damage Monitoring System

Damage Detection with sensors

BS

Micro Perspective for Damage Monitoring

**Figure 1.** A concept of damage monitoring and assessment in a large-scale natural disaster based on macro and micro

Aerial image

Image processing

Rescue Operation using ad hoc networks

Satellite image

technique are also presented.

studies on disaster communications.

Damage Estimation system


Emergency

call

perspectives

**2. Technologies for disaster communications**

32 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**2.1. A concept on damage monitoring in micro and micro perspectives**

networks described in this paper play a critical role for the micro perspective.

Macro Perspective for Damage Monitoring

The third-generation (3G) mobile systemshave provided the performance of up to 2 Mbps and various services in application systems [11]. Furthermore, current LTE (Long Term Evolution) and LTE-Advanced systems are providing a broadband mobile communications, leading to the fourth-generation (4G) cellular system, whichshould operate at the data rate of 100 Mbps or more [12, 13]. Since the latest mobile networks are challenging to provide a high data rate and a high capacity, those systems are required to operate at a higher carrier frequency and a large peak transmission power. A concept of virtual cellular system has been studiedand achieved to reduce the average transmission power compared with conventional cellular systems[14]. Thus, the current mobile systems have been developed focusing on high data rate and high capacity, under the policy of the best effort performance in ordinary conditions. However, in a disaster, it is strongly required for the communication systems to ensure connectivity even if difficult.

In the past,immediately after a large-scale natural disaster, massive access to communications systems occurred, and the systems lapsed into communications congestion, in the worst cases resulting in system failure. To both mitigate the congestion and prevent system failure, accessibilityingeneral channels for citizensis restricted into 1/n(Fig. 2). Since the regulation is mainly applied to the telephone call, data communications such as e-mail services might be maintained, even if taking a long delivery time. Meanwhile, prioritized channels have been set up in advance, to maintain connection in a disaster, but the number of the channels is not enough to transmit information from a large-scale damaged areas.

Multi-hopping is one solution to extend and maintain the coverage. A technical report of 3GPP (Third Generation Partnership Project) showeda scheme called ODMA (Opportunity Driven Multiple Access) to maintain high data rate in the edges of the coverage by relaying commu‐ nications (Fig.3)[15]. Mobile stations located in the high data-rate can access the Base Station (BS) directly. On the other hand, stations outside of the high data-rate cannot maintain the rate. They request a terminal located in the high data-rate area to relay their packets.

Similar idea was proposed, referred to as MRAC (Multi-hop Radio Access Cellular), which aims high speed, high capacity and wide area coverage by multi-hopping [16]. In the system, dedicated Repeater Stations (RS) are set up in a good propagation area, and the stations relay packets between user terminals and BS. In the event that a mobile station detects high propagation loss in the single-hop conditions to BS, the station selects a neighboring RS to relay packets. Hereby, MRAC is capable of expanding the coverage. However, since MRAC premisesmulti-hoping via RS, the restriction of the arrangement of RS reduces flexibility of the system operation. One solution is *ad hoc* networking, to build a network flexibly.

#### **2.3. Ad hoc networks**

Ad hoc network is a scheme to flexibly build a network without infrastructure facilities [17]. The networkis expected to maintain communications and to collect information even in a disaster. Figure 4 shows a model ofad hoc networks, where terminals are deployed and connected each other flexibly with wireless communications. For example, in rescue opera‐

for disaster communications. The hybrid network named Sphinx [22] aims to achieve high throughput and low power consumption. Concurrently, it addresses fairness for resource allocation, and resilience for mobility. The mobile stations operate in two modes, one is a cellular mode, and the other is a peer-to-peer mode(Fig. 5). When a mobile station communi‐ cates with the others located in the same cell, all flows are served in the peer-to-peer mode in the initial state. In the event that a mobile station detects degradation of the throughput in the

drivenroutingprotocolssuchasDSDV(DestinationSequenceDistanceVectorroutingprotocol)[20]andO LSR(OptimizedLinkStateRoutingprotocol)[21].Thenetworkschememayachieveflexiblenetwork.How

scaledisaster,eveninadhocnetworks.Inaddition,sincethelinksofadhocnetworksarevulnerable,massiv

routerequestorroutemaintenancepacketsmaybeinduced,resultinginheavytrafficcongestionandcom

Hybrid wireless networking schemes combiningcellularand multi‐hopping technique have been developedto aim high data rate, high capacity, wide area coverage and QoS control, not for disaster communications. The hybrid network named Sphinx [22] aims to achieve high throughput and low power consumption. Concurrently, it addresses fairness for resource allocation, and resilience for mobility. The mobile stations operate in two modes, one is a cellular mode, and the other is a peer‐to‐peer mode(Fig. 5). When a mobile station communicates with the others located in the same cell, all flows are served in the peer‐to‐peer mode in the initial state. In the event that a mobile station detects degradation of the throughput in the peer‐to‐peer mode, it requires BS to

Fig. 4. **Aconceptof***adhoc***networksforrescueoperations.**

Afflicted area Terminal

Mobile Base Station

Emergency Operation

http://dx.doi.org/10.5772/55513

35

Center

Multi-Tier Networks for Citywide Damage Monitoring in a Natural Disaster

Fig. 5. **Hybrid network model for cellular packet data network (Sphinx).**

Mobile Station

Cellular mode Peer-to-peer mode

Mobile Station

Cellular mode Peer-to-peer mode

iCAR(IntegratedCellularandAdhocRelayingSystem),aimstoavoidblockinganddroppingcommunicatio nsduetolocalizedcongestion,andfocusesontrafficloadbalancing[23].Thesysteminstallsadhocrelaysta tions(ARS),whichareplacedatstrategiclocationstodiverttrafficinone(possiblycongested)celltoanothe r(non‐congested)cell (Fig.6).TerminalsinacongestedcelltrytoaccessaBSofasurroundingnon‐ congestedcellviaanARS.Thoseschemesarefocusingonmaintainingthroughputandotherfeaturesinord

Another scheme, named iCAR (Integrated Cellular and Ad hoc Relaying System), aims to avoid blocking and dropping communications due to localized congestion, and focuses on traffic load balancing [23]. The system installs ad hoc relay stations (ARS), which are placed at strategic locations to divert traffic in one (possibly congested) cell to another (non-congested) cell (Fig. 6). Terminals in a congested cell try to access a BS of a surrounding non-congested cell via an ARS. Those schemes are focusing on maintaining throughput and other features in

ever,stablecommunicationsenvironmentcouldnotbeprovidedimmediatelyafteralarge‐

econtrolpacketssuchaseither

**2.4. Hybrid wireless networks** 

switch to the cellular mode.

Anotherscheme,named

municationsfailure.

peer-to-peer mode, it requires BS to switch to the cellular mode.

**BS** 

**Figure 5.** Hybrid network model for cellular packet data network (Sphinx).

**BS** 

**Figure 4.** A concept of *ad hoc* networks forrescue operations.

Figure 2. Traffic controlby reducing accessibility into 1/n in a disaster. **Figure 2.** Traffic controlby reducing accessibility into 1/n in a disaster.

**2.3. Ad hoc networks Figure 3.** Opportunity Driven Multiple Access (ODMA).

tions, the rescue team should work, sharing damage information among the team in the afflicted site.Ad hoc networking technique enables the team to access each other and to share information without infrastructures. The information could be forwarded to an emergency operation center through the mobile base station. Ad hoc network is a scheme to flexibly build a network without infrastructure facilities [17]. The networkis expected to maintain communications and to collect information even in a disaster. Figure 4 shows a model ofad hoc networks, where terminals are deployed and connected each other flexibly with wireless communications. For example, in rescue operations, the rescue team should work, sharing damage information among the team in the afflicted site.Ad hoc networking technique enables the team to access each other and to share information without infrastructures. The

Several protocols to build ad hoc networks flexibly and autonomously have been proposed based on the scheme ofon-demand driven routing protocols such as AODV (Adhoc Ondemand Distance Vector routing protocol) [18] and DSR (Dynamic Source Routing protocol) [19], and proactive table-driven routing protocols such as DSDV (Destination Sequence Distance Vector routing protocol)[20] and OLSR (Optimized Link State Routing protocol) [21]. The network scheme may achieve flexible network. However, stable communications envi‐ ronment could not be provided immediately after a large-scale disaster, even in ad hoc networks. In addition, since the links of ad hoc networks are vulnerable, massive control packets such as either route request or route maintenance packets may be induced, resulting in heavy traffic congestion and communications failure. information could be forwarded to an emergency operation center through the mobile base station. Several protocols to build ad hoc networks flexibly and autonomously have been proposed based on the scheme ofondemand driven routing protocols such as AODV (Adhoc On-demand Distance Vector routing protocol) [18] and DSR (Dynamic Source Routing protocol) [19], and proactive table-driven routing protocols such as DSDV (Destination Sequence Distance Vector routing protocol)[20] and OLSR (Optimized Link State Routing protocol) [21]. The network scheme may achieve flexible network. However, stable communications environment could not be provided immediately after a large-scale disaster, even in ad hoc networks. In addition, since the links of ad hoc networks are vulnerable, massive control packets such as either route request or route maintenance packets may be induced, resulting in heavy traffic congestion and communications failure. **2.4. Hybrid wireless networks** 

#### **2.4. Hybrid wireless networks** Hybrid wireless networking schemes combiningcellularand multi-hopping technique have been developedto aim high

Hybrid wireless networking schemes combiningcellularand multi-hopping technique have been developedto aim high data rate, high capacity, wide area coverage and QoS control, not named Sphinx [22] aims to achieve high throughput and low power consumption. Concurrently, it addresses fairness for resource allocation, and resilience for mobility. The mobile stations operate in two modes, one is a cellular mode, and the other is a peer-to-peer mode(Fig. 5). When a mobile station communicates with the others located in the same cell, all

throughput in the peer-to-peer mode, it requires BS to switch to the cellular mode.

data rate, high capacity, wide area coverage and QoS control, not for disaster communications. The hybrid network

flows are served in the peer-to-peer mode in the initial state. In the event that a mobile station detects degradation of the

for disaster communications. The hybrid network named Sphinx [22] aims to achieve high throughput and low power consumption. Concurrently, it addresses fairness for resource allocation, and resilience for mobility. The mobile stations operate in two modes, one is a cellular mode, and the other is a peer-to-peer mode(Fig. 5). When a mobile station communi‐ cates with the others located in the same cell, all flows are served in the peer-to-peer mode in the initial state. In the event that a mobile station detects degradation of the throughput in the peer-to-peer mode, it requires BS to switch to the cellular mode. developedto aim high data rate, high capacity, wide area coverage and QoS control, not for disaster communications. The hybrid network named Sphinx [22] aims to achieve high throughput and low power consumption. Concurrently, it addresses fairness for resource allocation, and resilience for mobility. The mobile stations operate in two modes, one is a cellular mode, and the other is a peer‐to‐peer mode(Fig. 5). When a mobile station communicates with the others located in the same cell, all flows are served in the peer‐to‐peer mode in the initial state. In the event that a mobile station detects degradation of the throughput in the peer‐to‐peer mode, it requires BS to

drivenroutingprotocolssuchasDSDV(DestinationSequenceDistanceVectorroutingprotocol)[20]andO LSR(OptimizedLinkStateRoutingprotocol)[21].Thenetworkschememayachieveflexiblenetwork.How

scaledisaster,eveninadhocnetworks.Inaddition,sincethelinksofadhocnetworksarevulnerable,massiv

routerequestorroutemaintenancepacketsmaybeinduced,resultinginheavytrafficcongestionandcom

ever,stablecommunicationsenvironmentcouldnotbeprovidedimmediatelyafteralarge‐

econtrolpacketssuchaseither

**2.4. Hybrid wireless networks** 

switch to the cellular mode.

municationsfailure.

Fig. 4. **Aconceptof***adhoc***networksforrescueoperations. Figure 4.** A concept of *ad hoc* networks forrescue operations.

tions, the rescue team should work, sharing damage information among the team in the afflicted site.Ad hoc networking technique enables the team to access each other and to share information without infrastructures. The information could be forwarded to an emergency

**BS** 

**BS** 

Base Station

Restricted transmission

Guaranteed

Low data-rate coverage

Low data-rate coverage

Restricted at 1/*n*

Ad hoc network is a scheme to flexibly build a network without infrastructure facilities [17]. The networkis expected to maintain communications and to collect information even in a disaster. Figure 4 shows a model ofad hoc networks, where terminals are deployed and connected each other flexibly with wireless communications. For example, in rescue operations, the rescue team should work, sharing damage information among the team in the afflicted site.Ad hoc networking technique enables the team to access each other and to share information without infrastructures. The

Local Exchange Station

Out-of-range

Out-of-range

To Core Networks

Several protocols to build ad hoc networks flexibly and autonomously have been proposed based on the scheme ofondemand driven routing protocols such as AODV (Adhoc On-demand Distance Vector routing protocol) [18] and DSR (Dynamic Source Routing protocol) [19], and proactive table-driven routing protocols such as DSDV (Destination Sequence Distance Vector routing protocol)[20] and OLSR (Optimized Link State Routing protocol) [21]. The network scheme may achieve flexible network. However, stable communications environment could not be provided immediately after a large-scale disaster, even in ad hoc networks. In addition, since the links of ad hoc networks are vulnerable, massive control packets such as either route request or route maintenance packets may be induced, resulting

Hybrid wireless networking schemes combiningcellularand multi-hopping technique have been developedto aim high data rate, high capacity, wide area coverage and QoS control, not for disaster communications. The hybrid network named Sphinx [22] aims to achieve high throughput and low power consumption. Concurrently, it addresses fairness for resource allocation, and resilience for mobility. The mobile stations operate in two modes, one is a cellular mode, and the other is a peer-to-peer mode(Fig. 5). When a mobile station communicates with the others located in the same cell, all flows are served in the peer-to-peer mode in the initial state. In the event that a mobile station detects degradation of the

Figure 2. Traffic controlby reducing accessibility into 1/n in a disaster.

Guaranteed

**Figure 2.** Traffic controlby reducing accessibility into 1/n in a disaster.

Channel Access

34 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

High data-rate coverage

High data-rate coverage

Several protocols to build ad hoc networks flexibly and autonomously have been proposed based on the scheme ofon-demand driven routing protocols such as AODV (Adhoc Ondemand Distance Vector routing protocol) [18] and DSR (Dynamic Source Routing protocol) [19], and proactive table-driven routing protocols such as DSDV (Destination Sequence Distance Vector routing protocol)[20] and OLSR (Optimized Link State Routing protocol) [21]. The network scheme may achieve flexible network. However, stable communications envi‐ ronment could not be provided immediately after a large-scale disaster, even in ad hoc networks. In addition, since the links of ad hoc networks are vulnerable, massive control packets such as either route request or route maintenance packets may be induced, resulting

information could be forwarded to an emergency operation center through the mobile base station.

Hybrid wireless networking schemes combiningcellularand multi-hopping technique have been developedto aim high data rate, high capacity, wide area coverage and QoS control, not

throughput in the peer-to-peer mode, it requires BS to switch to the cellular mode.

operation center through the mobile base station.

**2.3. Ad hoc networks** 

**Figure 3.** Opportunity Driven Multiple Access (ODMA).

Prioritized Channels

General Channels

Figure 3. Opportunity Driven Multiple Access (ODMA).

in heavy traffic congestion and communications failure.

**2.4. Hybrid wireless networks** 

in heavy traffic congestion and communications failure.

**2.4. Hybrid wireless networks**

Anotherscheme,named **Figure 5.** Hybrid network model for cellular packet data network (Sphinx).

iCAR(IntegratedCellularandAdhocRelayingSystem),aimstoavoidblockinganddroppingcommunicatio nsduetolocalizedcongestion,andfocusesontrafficloadbalancing[23].Thesysteminstallsadhocrelaysta tions(ARS),whichareplacedatstrategiclocationstodiverttrafficinone(possiblycongested)celltoanothe r(non‐congested)cell (Fig.6).TerminalsinacongestedcelltrytoaccessaBSofasurroundingnon‐ congestedcellviaanARS.Thoseschemesarefocusingonmaintainingthroughputandotherfeaturesinord Another scheme, named iCAR (Integrated Cellular and Ad hoc Relaying System), aims to avoid blocking and dropping communications due to localized congestion, and focuses on traffic load balancing [23]. The system installs ad hoc relay stations (ARS), which are placed at strategic locations to divert traffic in one (possibly congested) cell to another (non-congested) cell (Fig. 6). Terminals in a congested cell try to access a BS of a surrounding non-congested cell via an ARS. Those schemes are focusing on maintaining throughput and other features in

ordinary conditions, and such hybrid networks might be effective even in extraordinary conditions, if the system could be resilient to maintain communications.

SI: Sink node, R: Relay node, S: Sensor node

R5 R7 R8

S

Now, we discuss the network design of multi-tier networks to operate for damage monitoring. The architecture of the multi-tier networks is based on the hybrid network, which is configured with a centralized hierarchical network and *ad hoc* networks. In addition, to detect phenomena, wireless sensor networks are introduced and work with the hybrid network for damage

In case of large-scale natural disaster, information required for damage assessment changes as time goes by.The authorities need to comprehend the circumstances of damage based on multidirectional aspects. They acquire damage information comprehensively,make a strategy for emergency response, and carry out rescue operation quickly. Though the concept of both the macro and micro perspectives shown in the previous section is nontrivial, we focus on a network model based on the micro perspective to comprehend the conditions of individual

Figure 8 shows a network model todetect extraordinary phenomenaand to collect the infor‐ mation.The sensors are placed in houses, buildings and structures in a whole city. Information detected by sensors is transmitted to a CS through a base station in the centralized network. The centralized network combines *ad hoc* networking operation to enhance the connectivity, referred to as a hybrid network. The emergency operation center accesses the information

Damage detection in a disaster is performed with several sensors; seismic vibration is meas‐ ured with accelerometers installed in a structure. Meanwhile, wireless sensor networks (WSN) draw attention to detect several phenomena, temperature, humidity, brightness etc. in low cost. As the nodes of WSN contain accelerometers, the networks are capable of detecting seismic acceleration to assess the damage. In addition, lifeline facilities such as gas meters,

stored in the CS through the backbone network such as the Internet.

R1 R2 R3 R4

SI

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37

Multi-Tier Networks for Citywide Damage Monitoring in a Natural Disaster

R6

**Figure 7.** Wireless sensor networks deployed in a building

monitoring.

damages in an afflicted site.

**3.1. Damage detection with sensors**

**3. Multi-tier networks for damage monitoring**

**Figure 6.** Integrated cellular and ad hoc relay system (iCAR).

#### **2.5. Wireless sensor networks**

Wireless sensor networks premise that massive sensor nodes are deployed in the field and build a network autonomously.The networks transmit data from sensors to a sink node by multi-hopping,which are expected to collect information in several application systems such as environmental monitoring, structural health monitoring and so on. Since the nodes are usually restricted in CPU power and are operating by batteries, it is strongly required to reduce energy consumption [24]. Though several protocols have been proposed, they do not suffi‐ ciently consider how to operate in the seismic monitoring.

Assumingseismic monitoring, multiple sensor nodes are placed in buildings, bridges, or structures to detect seismic motion, temperature, or distortion of the structures. Furthermore, the system might be expected to find persons who trapped in the building.The reference [25] describes a seismic acceleration observed in the University of California, Irvine, where the acceleration includes the bandwidth of around 30Hz on the ground and 5Hz on the fourth floor. Thereby, the monitoring system is operating at the sampling rate of 200Hz. Health monitoring of the Golden Gate Bridge was carried out with wireless sensor networks [26]. They achieved the monitoring of the vibration on the bridge by multi-hopping of 46 hops at a sampling rate of 1 KHz. Then, issues to be taken into account in monitoring system are to provide a long time operation and to extend the coverage of damage monitoring in a largescale disaster.

As an example, Figure 7 shows an outline of the monitoring with a wireless sensor network in a building. The sensor network iscomposedof a number of sensor nodes (SNs), some relay nodes (RNs)and a sink node (SI) to gather data from SNs to SI. The network installs SI and RNs atstrategically designated positionsin advance on one hand. The SNs, on the other hand, are flexibly distributed in the building, and transmit data to RNs directly or via adjacent SNs by way of multi-hopping. The RN relays data to SI using a direct path or a multi-hopping path via RNs. Though, the SI is capable of collecting and storing the data, we have to study how to collect the information from a great number of sink nodes in a whole city.

SI: Sink node, R: Relay node, S: Sensor node

**Figure 7.** Wireless sensor networks deployed in a building

ordinary conditions, and such hybrid networks might be effective even in extraordinary

ARS ARS Non-congested cell Congested cell

Wireless sensor networks premise that massive sensor nodes are deployed in the field and build a network autonomously.The networks transmit data from sensors to a sink node by multi-hopping,which are expected to collect information in several application systems such as environmental monitoring, structural health monitoring and so on. Since the nodes are usually restricted in CPU power and are operating by batteries, it is strongly required to reduce energy consumption [24]. Though several protocols have been proposed, they do not suffi‐

Assumingseismic monitoring, multiple sensor nodes are placed in buildings, bridges, or structures to detect seismic motion, temperature, or distortion of the structures. Furthermore, the system might be expected to find persons who trapped in the building.The reference [25] describes a seismic acceleration observed in the University of California, Irvine, where the acceleration includes the bandwidth of around 30Hz on the ground and 5Hz on the fourth floor. Thereby, the monitoring system is operating at the sampling rate of 200Hz. Health monitoring of the Golden Gate Bridge was carried out with wireless sensor networks [26]. They achieved the monitoring of the vibration on the bridge by multi-hopping of 46 hops at a sampling rate of 1 KHz. Then, issues to be taken into account in monitoring system are to provide a long time operation and to extend the coverage of damage monitoring in a large-

As an example, Figure 7 shows an outline of the monitoring with a wireless sensor network in a building. The sensor network iscomposedof a number of sensor nodes (SNs), some relay nodes (RNs)and a sink node (SI) to gather data from SNs to SI. The network installs SI and RNs atstrategically designated positionsin advance on one hand. The SNs, on the other hand, are flexibly distributed in the building, and transmit data to RNs directly or via adjacent SNs by way of multi-hopping. The RN relays data to SI using a direct path or a multi-hopping path via RNs. Though, the SI is capable of collecting and storing the data, we have to study how to

collect the information from a great number of sink nodes in a whole city.

ARS

conditions, if the system could be resilient to maintain communications.

36 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**BS** 

**Figure 6.** Integrated cellular and ad hoc relay system (iCAR).

ciently consider how to operate in the seismic monitoring.

**2.5. Wireless sensor networks**

scale disaster.

#### **3. Multi-tier networks for damage monitoring**

Now, we discuss the network design of multi-tier networks to operate for damage monitoring. The architecture of the multi-tier networks is based on the hybrid network, which is configured with a centralized hierarchical network and *ad hoc* networks. In addition, to detect phenomena, wireless sensor networks are introduced and work with the hybrid network for damage monitoring.

In case of large-scale natural disaster, information required for damage assessment changes as time goes by.The authorities need to comprehend the circumstances of damage based on multidirectional aspects. They acquire damage information comprehensively,make a strategy for emergency response, and carry out rescue operation quickly. Though the concept of both the macro and micro perspectives shown in the previous section is nontrivial, we focus on a network model based on the micro perspective to comprehend the conditions of individual damages in an afflicted site.

Figure 8 shows a network model todetect extraordinary phenomenaand to collect the infor‐ mation.The sensors are placed in houses, buildings and structures in a whole city. Information detected by sensors is transmitted to a CS through a base station in the centralized network. The centralized network combines *ad hoc* networking operation to enhance the connectivity, referred to as a hybrid network. The emergency operation center accesses the information stored in the CS through the backbone network such as the Internet.

#### **3.1. Damage detection with sensors**

Damage detection in a disaster is performed with several sensors; seismic vibration is meas‐ ured with accelerometers installed in a structure. Meanwhile, wireless sensor networks (WSN) draw attention to detect several phenomena, temperature, humidity, brightness etc. in low cost. As the nodes of WSN contain accelerometers, the networks are capable of detecting seismic acceleration to assess the damage. In addition, lifeline facilities such as gas meters, tiernetworkstooperatefordamagemonitoring.Thearchitectureofthemulti‐

Now,wediscussthenetworkdesignofmulti‐

withthehybridnetworkfordamagemonitoring.

dividualdamagesinanafflictedsite.

**3. MULTI-TIER NETWORKS FOR DAMAGE MONITORING** 

tiernetworksisbasedonthehybridnetwork,whichisconfiguredwithacentralizedhierarchicalnetworka nd*adhoc*networks.Inaddition,todetectphenomena,wirelesssensornetworksareintroducedandwork

Incase of large‐ scalenaturaldisaster,informationrequiredfordamageassessmentchangesastimegoesby.Theauthoriti esneedtocomprehendthecircumstancesofdamagebasedonmultidirectionalaspects.Theyacquireda mageinformationcomprehensively,makeastrategyforemergencyresponse,andcarryoutrescueopera

nontrivial,wefocusonanetworkmodelbasedonthemicroperspectivetocomprehendtheconditionsofin

Fig. 8. **Network model for multi‐tier damage monitoring in a natural disaster. Figure 8.** Network model for multi-tier damage monitoring in a natural disaster.

electricity meters and water meters include various kinds of sensors to detect conditions of facilities. nsorsareplacedinhouses,buildingsandstructuresinawholecity.Informationdetectedbysensorsistrans mittedtoaCS throughabasestationinthecentralizednetwork.Thecentralizednetworkcombines*adhoc*networkingo

the network to collect damage information quickly considering how to maintain connec‐

BS

<sup>T</sup> <sup>T</sup> <sup>T</sup> <sup>T</sup> <sup>T</sup>

CS

The network model is configured with two layers:the upper layer, composed of a CS and multiple BS connected by either wireless or wired channels, and the lower network,that contains a great number of terminals. Assuming that those terminals are placed in all residences and the collected information is restricted to emergency communications, the volume of the traffic in a cell is almost predictable, and we can design the channel capacity of the network. One concern is, however, those channels are vulnerable in a natural disaster. Especially, cables of wired networks may easily suffer damage. Thereby we should design the network to collect damage information quickly considering how to maintain

The lower layer network, consists of the BS and multiple terminals in each cell, where the BS and terminals communicate in TD-CDMA (Time Division and Code Division Multiple Access) mode at 2.4 GHz, which is designed based on CDMA (Code Division Multiple Access) and TDMA (Time Division Multiple Access). The output power is 10 mW or less for the

The lower layer network, consists of the BS and multiple terminals in each cell, where the BS and terminals communicate in TD-CDMA (Time Division and Code Division Multiple Access) mode at 2.4 GHz, which is designed based on CDMA (Code Division Multiple Access) and TDMA (Time Division Multiple Access). The output power is 10 mW or less for the commu‐

**Upperlayer Lowerlayer**

Bandwidth <200kHz <1.5MHz Outputpower 1W 10mW

Accesscontrol Polling TD-CDMA Datarate 288kbps 19.2kbps(Fw)

Table 1. Air interface parameters of channels in the upper and lower layers (Fw: Forward-link; Rw: Reverse-link)

Modulation /4-shiftQPSK DBPSK(Fw),DQPSK(Rv)

Chiprate 1.2288Mcps 1.2288Mcps Datasize 156B 256B Framelength 320ms 320ms Datarate 19.2ksps 9.6ksps Processinggain 64 128

2.2899GHz(Rv)

2.402GHz(Fw) 2.482GHz(Rv)

**Upperlayer Lowerlayer** 

2.402GHz(Fw) 2.482GHz(Rv)

CS: Control Station BS: Base Station T: Terminal

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39

Multi-Tier Networks for Citywide Damage Monitoring in a Natural Disaster

9.6kbps(Rv)

9.6kbps(Rv)

**Forward-link Reverse-link** 

**Forward-link Reverse-link**

2.1099GHz(Fw) 2.2899GHz(Rv)

Figure 9. Centralized hierarchical network model.

connections in disaster circumstances.

communications range of 300 m.

Bandwidth <200kHz <1.5MHz Outputpower 1W 10mW

Frequency 2.1099GHz(Fw)

Accesscontrol Polling TD-CDMA Datarate 288kbps 19.2kbps(Fw)

Table 2. Data transmission parameters of the CDMA channels.

**Table 2.** Data transmission parameters of the CDMA channels.

Modulation π/4-shiftQPSK DBPSK(Fw),DQPSK(Rv)

**Table 1.** Air interface parameters of channels in the upper and lower layers (Fw: Forward-link; Rw: Reverse-link)

Chiprate 1.2288Mcps 1.2288Mcps Datasize 156B 256B Framelength 320ms 320ms Datarate 19.2ksps 9.6ksps Processinggain 64 128

tions in disaster circumstances.

Upper Layer

Lower Layer

**Figure 9.** Centralized hierarchical network model.

nications range of 300 m.

Frequency

Figure8showsanetworkmodeltodetectextraordinaryphenomenaandtocollecttheinformation.These

Since the communications range of a link in sensor networks is short, e.g., 20 m normally, or 100 m at most, multi-hopping technique is used to expand the coverage. Furthermore, each sensor node operates with a small battery, and the CPU power is restricted to control the operation including the calculation of a routing path. Provided that the scale of the network becomes larger, traffic to relay packets increases and induces large power consumption. perationtoenhancetheconnectivity,referredtoasahybridnetwork.Theemergencyoperationcenterac cessestheinformationstoredintheCSthroughthebackbonenetworksuchastheInternet. **3.1.Damagedetectionwithsensors** Damagedetectioninadisasterisperformedwithseveralsensors;seismicvibrationismeasuredwithaccel erometersinstalledinastructure.Meanwhile,wirelesssensornetworks(WSN)drawattentiontodetects

#### **3.2. Centralized hierarchical network for damage monitoring** everalphenomena,temperature,humidity,brightnessetc.inlowcost.AsthenodesofWSNcontainaccele rometers,thenetworksarecapableofdetectingseismicaccelerationtoassessthedamage.Inaddition,lif

A centralized hierarchical network is composed of multiple terminals and a base station in a cell, and those BS access a CS to transmit the information gathered from terminals (Fig. 9). In ordinary conditions,the network is effective in transmitting packets quickly. If the network is available for quick accurate damage monitoring even in extraordinary conditions, the model would be employed in the monitoring system. However, such network has suffered from disconnection between terminals and BS, and lapsed into communications congestion in a previous large-scale disaster. In collecting damage information and emergency messages from terminals placed in a whole city, it is strongly required to transmit packets efficiently from distributed massive terminals to BS, and from BS to CS for swift rescue operations. elinefacilitiessuchasgasmeters,electricitymetersandwatermetersincludevariouskindsofsensorstode tectconditionsoffacilities. Sincethecommunicationsrangeofalinkinsensornetworksisshort,e.g.,20mnormally,or100matmost,m ulti‐ hoppingtechniqueisusedtoexpandthecoverage.Furthermore,eachsensornodeoperateswithasmallb attery,andtheCPUpowerisrestrictedtocontroltheoperationincludingthecalculationofaroutingpath.P

The network model is configured with two layers:the upper layer, composed of a CS and multiple BS connected by either wireless or wired channels, and the lower network,that contains a great number of terminals. Assuming that those terminals are placed in all residences and the collected information is restricted to emergency communications, the volume of the traffic in a cell is almost predictable, and we can design the channel capacity of the network. One concern is, however, those channels are vulnerable in a natural disaster. Especially, cables of wired networks may easily suffer damage. Thereby we should design

Figure 9. Centralized hierarchical network model. **Figure 9.** Centralized hierarchical network model.

electricity meters and water meters include various kinds of sensors to detect conditions of

Fig. 8. **Network model for multi‐tier damage monitoring in a natural disaster.**

AP/SI: Access Point and Sink Node, S: Sensors

Sensor Networks <sup>S</sup> <sup>S</sup> <sup>S</sup> <sup>S</sup> <sup>S</sup> <sup>S</sup> <sup>S</sup> <sup>S</sup> <sup>S</sup> AP/SI Hybrid Network

OC: Operation Center, CS: Control Station, BS: Base Station,

Figure8showsanetworkmodeltodetectextraordinaryphenomenaandtocollecttheinformation.These nsorsareplacedinhouses,buildingsandstructuresinawholecity.Informationdetectedbysensorsistrans

throughabasestationinthecentralizednetwork.Thecentralizednetworkcombines*adhoc*networkingo perationtoenhancetheconnectivity,referredtoasahybridnetwork.Theemergencyoperationcenterac

Damagedetectioninadisasterisperformedwithseveralsensors;seismicvibrationismeasuredwithaccel erometersinstalledinastructure.Meanwhile,wirelesssensornetworks(WSN)drawattentiontodetects everalphenomena,temperature,humidity,brightnessetc.inlowcost.AsthenodesofWSNcontainaccele rometers,thenetworksarecapableofdetectingseismicaccelerationtoassessthedamage.Inaddition,lif elinefacilitiessuchasgasmeters,electricitymetersandwatermetersincludevariouskindsofsensorstode

Sincethecommunicationsrangeofalinkinsensornetworksisshort,e.g.,20mnormally,or100matmost,m

hoppingtechniqueisusedtoexpandthecoverage.Furthermore,eachsensornodeoperateswithasmallb attery,andtheCPUpowerisrestrictedtocontroltheoperationincludingthecalculationofaroutingpath.P

cessestheinformationstoredintheCSthroughthebackbonenetworksuchastheInternet.

Since the communications range of a link in sensor networks is short, e.g., 20 m normally, or 100 m at most, multi-hopping technique is used to expand the coverage. Furthermore, each sensor node operates with a small battery, and the CPU power is restricted to control the operation including the calculation of a routing path. Provided that the scale of the network becomes larger, traffic to relay packets increases and induces large power consumption.

A centralized hierarchical network is composed of multiple terminals and a base station in a cell, and those BS access a CS to transmit the information gathered from terminals (Fig. 9). In ordinary conditions,the network is effective in transmitting packets quickly. If the network is available for quick accurate damage monitoring even in extraordinary conditions, the model would be employed in the monitoring system. However, such network has suffered from disconnection between terminals and BS, and lapsed into communications congestion in a previous large-scale disaster. In collecting damage information and emergency messages from terminals placed in a whole city, it is strongly required to transmit packets efficiently from

distributed massive terminals to BS, and from BS to CS for swift rescue operations.

The network model is configured with two layers:the upper layer, composed of a CS and multiple BS connected by either wireless or wired channels, and the lower network,that contains a great number of terminals. Assuming that those terminals are placed in all residences and the collected information is restricted to emergency communications, the volume of the traffic in a cell is almost predictable, and we can design the channel capacity of the network. One concern is, however, those channels are vulnerable in a natural disaster. Especially, cables of wired networks may easily suffer damage. Thereby we should design

**3.2. Centralized hierarchical network for damage monitoring**

**Figure 8.** Network model for multi-tier damage monitoring in a natural disaster.

**3. MULTI-TIER NETWORKS FOR DAMAGE MONITORING** 

tiernetworksisbasedonthehybridnetwork,whichisconfiguredwithacentralizedhierarchicalnetworka nd*adhoc*networks.Inaddition,todetectphenomena,wirelesssensornetworksareintroducedandwork

Incase of large‐ scalenaturaldisaster,informationrequiredfordamageassessmentchangesastimegoesby.Theauthoriti esneedtocomprehendthecircumstancesofdamagebasedonmultidirectionalaspects.Theyacquireda mageinformationcomprehensively,makeastrategyforemergencyresponse,andcarryoutrescueopera tionquickly.Thoughtheconceptofboththemacroandmicroperspectivesshownintheprevioussectionis nontrivial,wefocusonanetworkmodelbasedonthemicroperspectivetocomprehendtheconditionsofin

CS

BS

tiernetworkstooperatefordamagemonitoring.Thearchitectureofthemulti‐

Backbone

38 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

Now,wediscussthenetworkdesignofmulti‐

withthehybridnetworkfordamagemonitoring.

dividualdamagesinanafflictedsite.

OC

facilities.

**3.1.Damagedetectionwithsensors**

tectconditionsoffacilities.

ulti‐

mittedtoaCS

the network to collect damage information quickly considering how to maintain connec‐ tions in disaster circumstances. either wireless or wired channels, and the lower network,that contains a great number of terminals. Assuming that those terminals are placed in all residences and the collected information is restricted to emergency communications, the volume of the traffic in a cell is almost predictable, and we can design the channel capacity of the network. One concern

The network model is configured with two layers:the upper layer, composed of a CS and multiple BS connected by

is, however, those channels are vulnerable in a natural disaster. Especially, cables of wired networks may easily suffer

**Upperlayer Lowerlayer** 

The lower layer network, consists of the BS and multiple terminals in each cell, where the BS and terminals communicate in TD-CDMA (Time Division and Code Division Multiple Access) mode at 2.4 GHz, which is designed based on CDMA (Code Division Multiple Access) and TDMA (Time Division Multiple Access). The output power is 10 mW or less for the commu‐ nications range of 300 m. damage. Thereby we should design the network to collect damage information quickly considering how to maintain connections in disaster circumstances. The lower layer network, consists of the BS and multiple terminals in each cell, where the BS and terminals communicate in TD-CDMA (Time Division and Code Division Multiple Access) mode at 2.4 GHz, which is designed based on CDMA (Code Division Multiple Access) and TDMA (Time Division Multiple Access). The output power is 10 mW or less for the communications range of 300 m.


**Table 1.** Air interface parameters of channels in the upper and lower layers (Fw: Forward-link; Rw: Reverse-link) Framelength 320ms 320ms


Datarate 19.2ksps 9.6ksps

**Table 2.** Data transmission parameters of the CDMA channels.

Figure 10.Channel operation in TD-CDMA **Figure 10.** Channel operation in TD-CDMA

The experimental system was designed to contain 256 terminals in a cell. The communications system of the radio channels connecting terminals with BS in the lower layer employs CDMA technology operating at 2.4GHz. The communications system also introducesthe time division mode,hence, referred to as TD-CDMA. Figure 10shows the time chart ofthe TD-CDMA operation. The multiplexed CDMA channels are divided into 32 timeslots of 320ms, to operate in the time-division-multiple-access mode. A group of 8 terminals, e.g. terminals 1 through 8, is assigned to one timeslot. 8 terminals are invoked at the timing of the #1 slot, and transmit data to the BS via CDMA channels. Thus, the BS is capable of collecting data from 256 (832) terminals in one TD-cycle of 10.24 seconds. The parameters of CDMAchannelsand data transmission are shown in Table 1 and 2. **3.3. Hybrid wireless monitoring enhanced with** *ad hoc* **networks**  The experimental system was designed to contain 256 terminals in a cell. The communications system of the radio channels connecting terminals with BS in the lower layer employs CDMA technology operating at 2.4GHz. The communications system also introducesthe time division mode,hence, referred to as TD-CDMA. Figure 10shows the time chart ofthe TD-CDMA operation. The multiplexed CDMA channels are divided into 32 timeslots of 320ms, to operate in the time-division-multiple-access mode. A group of 8 terminals, e.g. terminals 1 through 8, is assigned to one timeslot. 8 terminals are invoked at the timing of the #1 slot, and transmit data to the BS via CDMA channels. Thus, the BS is capable of collecting data from 256 (8´32) terminals in one TD-cycle of 10.24 seconds. The parameters of CDMAchannelsand data transmission are shown in Table 1 and 2.

A centralized network showsa good performance for damage monitoring in conditions where the links between a base

Provided that the node discovers a node which can access BS directly or other nodes which already found a route to BS, the node requests one of the neighboring nodes to forward damage

Fig. 13. **Hybridwirelessnetworkenhancedwith***adhoc***networks.**

CS: Central Control Station, BS: Base Station, TM: Terminal, S: Sensor

CS CH

AD mode S S TM

CS CH

BS mode

Multi-Tier Networks for Citywide Damage Monitoring in a Natural Disaster

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41

BS mode

Thehybridnetwork(Fig.11)combinesbothschemesofthecentralizedhierarchicalnetwork(CHmode)co nnectingBSandnodesdirectly,andthe*adhoc*networks(ADmode)connectingnodeseachother.Ifthecon ditionofthelinkbetweenBSandanodeisgettingworseandtheconnectioncannotmaintain,thenodeshift stheoperationtothe*adhoc*modeandaccessesaneighboringnode,wherethenoderelaypacketstoBS. Innormalconditions,mostofnodesaccessBSdirectlyinCellularmode.Ifadisasteroccursandseverallinks betweenBSandnodesaredisconnectedduetodamageorobstacles,thosenodesswitchto*adhoc*mode,a

AD mode S S TM

CS: Central Control Station, BS: Base Station, TM: Terminal, S: Sensor

To build a connection in AD mode of the hybrid network, each node, which cannot access BS directly, needs to discover a route to reach BS. Every node operating in either cellular or *ad hoc*mode (CH or AD mode) periodically transmits a control packet. The control packet includes the number of hops (Hop-CNT) to reach BS, the addresses of source, destination, sender and

f

k

G1

BS

Hop=1

G2

Hop=1

hopping.ProvidedthatthenodediscoversanodewhichcanaccessBSdirectlyorothernodeswhichalready

TobuildaconnectioninADmodeofthehybridnetwork,eachnode,whichcannotaccessBSdirectly,needst odiscoveraroutetoreachBS.Everynodeoperatingineithercellularor*adhoc*mode(CHorADmode)period

Fig. 14. **Ascenarioinroutediscovery.**

g

Inthe route discovery, a node (node-s), which cannot access BS, monitors communication of neighboring nodes. Overhearing a packet, the node checks Hop-CNT of the received packet, and knows how many hops are required to reach BS. It selects a node (node-i) as the next hop node based on the received Hop-CNT. The node-s records the address and the Hop-CNT of node-I; receiving a packet from node-s, the node-i forwards the packet since it already knows

s),whichcannotaccessBS,monitorscommunicationofneighboringnodes.Overhearingapacket,thenod

CNTofnode‐I; receivingapacketfromnode‐s,thenode‐

snoticesitcannotoverhearthecommunicationsfromthenoderecordedinitsroutingtableduringthetime

CNTofthereceivedpacket,andknowshowmanyhopsarerequiredtoreachBS.Itselectsanode(node‐ i)asthenexthopnodebasedonthereceivedHop‐CNT.Thenode‐srecordstheaddressandtheHop‐

Providedthatnode‐sdetectsmultiplenodeswhichcanreachBS,itrecordsthenodes'addressandHop‐ CNT,andselectsanodewhichhastheshortesthoppingrange.Ifmultiplenodesarefoundwhichhavethesh

iforwardsthepacketsinceitalreadyknowsaroutetoreachtheBS in a multi‐hoping.

j

*k*+1

s

u

**Figure 12.** A scenario in route discovery.

*k*+1

*<sup>k</sup>*+2 *k*+1

ortestroute,itmayselectoneofthemrandomly.Providedthatnode‐

a route to reach the BS in a multi-hoping.

foundaroutetoBS,thenoderequestsoneoftheneighboringnodestoforwarddamage data toBS.

icallytransmitsacontrolpacket.Thecontrolpacketincludesthenumberofhops(Hop‐ CNT)toreachBS,theaddressesofsource,destination,senderandreceivernodes (Fig.12).

**Figure 11.** Hybrid wireless network enhanced with *ad hoc* networks.

h t

k+1

k k

i

*k*+1

data to BS.

Intheroutediscovery,anode(node‐

echecksHop‐

tolive(TTL),node‐

receiver nodes (Fig.12).

ndattempttobuildaroutetoBSbywayofmulti‐

The hybridnetwork(Fig. 11) combines both schemes of the centralized hierarchical network (CH mode) connecting BS and nodes directly, and the *ad hoc* networks (AD mode) connecting nodes each other. If the condition ofthe link between BS and a node is getting worse and the connection cannot maintain, the node shifts the operation to the *ad hoc* mode and

In normal conditions, most of nodes access BS directly in Cellular mode. Ifa disaster occurs and several links between BS andnodesare disconnected due todamage or obstacles, those nodes switch to *ad hoc* mode, and attempt to build a route to BS by way of multi-hopping. Provided that the node discovers a node which can access BS directly or other nodes which

already found a route to BS, the node requests one of the neighboring nodes to forward damage data to BS.

#### station and terminals are maintained. *Ad hoc* networking, on the other hand, allows a node to rebuild a route by alternative links even if the connection of the links may not be maintained. However, the links of *ad hoc* networks are **3.3. Hybrid wireless monitoring enhanced with** *ad hoc* **networks**

vulnerabledue to not only mobility or limited power but also interferences or deteriorated propagation conditions. Thereby, ahybrid wireless network combining *adhoc*networks and a centralized network has drawn attention for disaster communications [27]. CS CH BS mode A centralized network showsa good performance for damage monitoring in conditions where the links between a base station and terminals are maintained. *Ad hoc* networking, on the other hand, allows a node to rebuild a route by alternative links even if the connection of the links may not be maintained. However, the links of *ad hoc* networks are vulnerabledue to not only mobility or limited power but also interferences or deteriorated propagation conditions. Thereby, ahybrid wireless network combining *adhoc*networks and a centralized network has drawn attention for disaster communications [27].

CS: Central Control Station, BS: Base Station, TM: Terminal, S: Sensor AD mode S S TM The hybridnetwork(Fig. 11) combines both schemes of the centralized hierarchical network (CH mode) connecting BS and nodes directly, and the *ad hoc* networks (AD mode) connecting nodes each other. If the condition ofthe link between BS and a node is getting worse and the connection cannot maintain, the node shifts the operation to the *ad hoc* mode and accesses a neighboring node, where the node relay packets to BS.

Figure 11.Hybrid wireless network enhanced with *ad hoc* networks. In normal conditions, most of nodes access BS directly in Cellular mode. Ifa disaster occurs and several links between BS andnodesare disconnected due todamage or obstacles, those nodes switch to *ad hoc* mode, and attempt to build a route to BS by way of multi-hopping.

accesses a neighboring node, where the node relay packets to BS.

BS mode

Thehybridnetwork(Fig.11)combinesbothschemesofthecentralizedhierarchicalnetwork(CHmode)co nnectingBSandnodesdirectly,andthe*adhoc*networks(ADmode)connectingnodeseachother.Ifthecon CS: Central Control Station, BS: Base Station, TM: Terminal, S: Sensor

ditionofthelinkbetweenBSandanodeisgettingworseandtheconnectioncannotmaintain,thenodeshift

Innormalconditions,mostofnodesaccessBSdirectlyinCellularmode.Ifadisasteroccursandseverallinks

stheoperationtothe*adhoc*modeandaccessesaneighboringnode,wherethenoderelaypacketstoBS. **Figure 11.** Hybrid wireless network enhanced with *ad hoc* networks.

Figure 10.Channel operation in TD-CDMA

Term 1 Term 2 Term 3 Term 4 Term 5 Term 6 Term 7 Term 8

40 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**#1 Slot #2 #32**

TD Cycle

Term 10 Term 11 Term 12

Term 9

Term 13

Term 14 Term 15

Term 16

**CH-2 CH-3 CH-4 CH-5 CH-6 CH-7 CH-8** 

CDMA Channels

**Figure 10.** Channel operation in TD-CDMA

**CH-1** 

communications [27].

drawn attention for disaster communications [27].

neighboring node, where the node relay packets to BS.

**3.3. Hybrid wireless monitoring enhanced with** *ad hoc* **networks**

transmission are shown in Table 1 and 2.

CDMAchannelsand data transmission are shown in Table 1 and 2.

A centralized network showsa good performance for damage monitoring in conditions where the links between a base station and terminals are maintained. *Ad hoc* networking, on the other hand, allows a node to rebuild a route by alternative links even if the connection of the links may not be maintained. However, the links of *ad hoc* networks are vulnerabledue to not only mobility or limited power but also interferences or deteriorated propagation conditions. Thereby, ahybrid wireless network combining *adhoc*networks and a centralized network has

The experimental system was designed to contain 256 terminals in a cell. The communications system of the radio channels connecting terminals with BS in the lower layer employs CDMA technology operating at 2.4GHz. The communications system also introducesthe time division mode,hence, referred to as TD-CDMA. Figure 10shows the time chart ofthe TD-CDMA operation. The multiplexed CDMA channels are divided into 32 timeslots of 320ms, to operate in the time-division-multiple-access mode. A group of 8 terminals, e.g. terminals 1 through 8, is assigned to one timeslot. 8 terminals are invoked at the timing of the #1 slot, and transmit data to the BS via CDMA channels. Thus, the BS is capable of collecting data from 256 (8´32) terminals in one TD-cycle of 10.24 seconds. The parameters of CDMAchannelsand data

0 0.32 0.64 10.24 Time

Figure 11.Hybrid wireless network enhanced with *ad hoc* networks.

CS: Central Control Station, BS: Base Station, TM: Terminal, S: Sensor

In normal conditions, most of nodes access BS directly in Cellular mode. Ifa disaster occurs and several links between BS andnodesare disconnected due todamage or obstacles, those nodes switch to *ad hoc* mode, and attempt to build a route to BS by way of multi-hopping.

AD mode S S TM

CS CH

The hybridnetwork(Fig. 11) combines both schemes of the centralized hierarchical network (CH mode) connecting BS and nodes directly, and the *ad hoc* networks (AD mode) connecting nodes each other. If the condition ofthe link between BS and a node is getting worse and the connection cannot maintain, the node shifts the operation to the *ad hoc* mode and accesses a

accesses a neighboring node, where the node relay packets to BS.

**3.3. Hybrid wireless monitoring enhanced with** *ad hoc* **networks** 

BS mode

chart ofthe TD-CDMA operation. The multiplexed CDMA channels are divided into 32 timeslots of 320ms, to operate in

(seconds)

**#1 Slot**

A centralized network showsa good performance for damage monitoring in conditions where the links between a base station and terminals are maintained. *Ad hoc* networking, on the other hand, allows a node to rebuild a route by alternative links even if the connection of the links may not be maintained. However, the links of *ad hoc* networks are vulnerabledue to not only mobility or limited power but also interferences or deteriorated propagation conditions. Thereby, ahybrid wireless network combining *adhoc*networks and a centralized network has drawn attention for disaster

The hybridnetwork(Fig. 11) combines both schemes of the centralized hierarchical network (CH mode) connecting BS and nodes directly, and the *ad hoc* networks (AD mode) connecting nodes each other. If the condition ofthe link between BS and a node is getting worse and the connection cannot maintain, the node shifts the operation to the *ad hoc* mode and

In normal conditions, most of nodes access BS directly in Cellular mode. Ifa disaster occurs and several links between BS andnodesare disconnected due todamage or obstacles, those nodes switch to *ad hoc* mode, and attempt to build a route to BS by way of multi-hopping. Provided that the node discovers a node which can access BS directly or other nodes which

already found a route to BS, the node requests one of the neighboring nodes to forward damage data to BS.

The experimental system was designed to contain 256 terminals in a cell. The communications system of the radio channels connecting terminals with BS in the lower layer employs CDMA technology operating at 2.4GHz. The communications system also introducesthe time division mode,hence, referred to as TD-CDMA. Figure 10shows the time Provided that the node discovers a node which can access BS directly or other nodes which already found a route to BS, the node requests one of the neighboring nodes to forward damage data to BS. betweenBSandnodesaredisconnectedduetodamageorobstacles,thosenodesswitchto*adhoc*mode,a ndattempttobuildaroutetoBSbywayofmulti‐ hopping.ProvidedthatthenodediscoversanodewhichcanaccessBSdirectlyorothernodeswhichalready

the time-division-multiple-access mode. A group of 8 terminals, e.g. terminals 1 through 8, is assigned to one timeslot. 8 terminals are invoked at the timing of the #1 slot, and transmit data to the BS via CDMA channels. Thus, the BS is capable of collecting data from 256 (832) terminals in one TD-cycle of 10.24 seconds. The parameters of To build a connection in AD mode of the hybrid network, each node, which cannot access BS directly, needs to discover a route to reach BS. Every node operating in either cellular or *ad hoc*mode (CH or AD mode) periodically transmits a control packet. The control packet includes the number of hops (Hop-CNT) to reach BS, the addresses of source, destination, sender and receiver nodes (Fig.12). foundaroutetoBS,thenoderequestsoneoftheneighboringnodestoforwarddamage data toBS. TobuildaconnectioninADmodeofthehybridnetwork,eachnode,whichcannotaccessBSdirectly,needst odiscoveraroutetoreachBS.Everynodeoperatingineithercellularor*adhoc*mode(CHorADmode)period icallytransmitsacontrolpacket.Thecontrolpacketincludesthenumberofhops(Hop‐ CNT)toreachBS,theaddressesofsource,destination,senderandreceivernodes (Fig.12).

Fig. 14. **Ascenarioinroutediscovery. Figure 12.** A scenario in route discovery.

Intheroutediscovery,anode(node‐ s),whichcannotaccessBS,monitorscommunicationofneighboringnodes.Overhearingapacket,thenod echecksHop‐ CNTofthereceivedpacket,andknowshowmanyhopsarerequiredtoreachBS.Itselectsanode(node‐ i)asthenexthopnodebasedonthereceivedHop‐CNT.Thenode‐srecordstheaddressandtheHop‐ CNTofnode‐I; receivingapacketfromnode‐s,thenode‐ Inthe route discovery, a node (node-s), which cannot access BS, monitors communication of neighboring nodes. Overhearing a packet, the node checks Hop-CNT of the received packet, and knows how many hops are required to reach BS. It selects a node (node-i) as the next hop node based on the received Hop-CNT. The node-s records the address and the Hop-CNT of node-I; receiving a packet from node-s, the node-i forwards the packet since it already knows a route to reach the BS in a multi-hoping.

Providedthatnode‐sdetectsmultiplenodeswhichcanreachBS,itrecordsthenodes'addressandHop‐ CNT,andselectsanodewhichhastheshortesthoppingrange.Ifmultiplenodesarefoundwhichhavethesh

snoticesitcannotoverhearthecommunicationsfromthenoderecordedinitsroutingtableduringthetime

iforwardsthepacketsinceitalreadyknowsaroutetoreachtheBS in a multi‐hoping.

ortestroute,itmayselectoneofthemrandomly.Providedthatnode‐

tolive(TTL),node‐

Provided that node-s detects multiple nodes which can reach BS, it records the nodes' address and Hop-CNT, and selects a node which has the shortest hopping range. If multiple nodes are found whichhave the shortest route, it may select one of them randomly. Provided that nodes notices it cannot overhear the communicationsfromthe node recorded in its routing table during the time to live (TTL), node-s decides the node is not available and deletes the record of the node in the table. The value of TTL is designated as a system parameter in advance.

Upper Layer Lower Layer

Base Station

Multi-Tier Networks for Citywide Damage Monitoring in a Natural Disaster

Lower layer cell

(Fig. 13). The system employs 2.1 GHz radio communications for

Terminals

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43

Lower layer Cell

The experimental system comprises two-tier networks to cover a total of 250k terminals in an

the upper layer and 2.4GHz CDMA forthe lower layer, and gathers data from gas meters, water meters and extra signals installed in houses to comprehend the conditions of lifeline facilities.

The upper layer network was designed to covers a whole citywith1024 cells.The central control station accesses the BS of each cell with a TDM (Time Division Multiplexing) wireless system. The upper layer radio system employed a narrow-band radio communications of 2.1GHz to connect CS and multiple BS, where the output power is 1 W, to cover a long distance. The air interface parameters are listed in Table 1. CS and BS of the upper layer contain a database server (Fig. 14):data transmitted from terminals to BS are stored in the database, and the server of BS provides data according to the requirement of CS. The CS stores the data in the database

The lower layer network consists of BS and multiple terminals in a cell, where the BS and terminals communicate in TD-CDMA at 2.4 GHz, combining CDMA and TDMA technologies. The output power is 10 mW or less in the communications range of 300 m. Though the experimental system was designed to contain 256 terminals in a cell, 128 terminals were actually installed in a residential area. The interface unit contained ina terminal is connected to a liquefied petroleum gas (LPG) meter, natural gas (NG) meter, water meter and additional signals (Fig. 14). Figure 15 shows the experimental CDMA and interface units connecting with

Frame error rate (FER) is defined as the rate of communications failure in two-way transmis‐ sion. The experiment observed and recorded the number of communications failure in each of 128 terminals, as in Figure 16, where the FER is indicated on the average of 128 terminals. The average FER was approximately 1.5×10−3. Thereby, the experiment showed the CDMA system achieved a low FER on the order of 10−3 even in the output power of 10mW, where the

Control Station

**Figure 13.** A model of acentralized hierarchical damage monitoring system.

and provides the data in an application system.

communications range is within 300m.

urban area of about 260 km2

meters.

Provided that a certain node transmits a packet, its Hop-CNT is set up at the value increment‐ ing the value of the next hop node. Thereby, the further neighboring nodes can overhear the communications and discover an available next hop node. Node-i is transmitting a packet to node-f at Hop-CNT=k. node-j is also transmitting at k+1. node-s overhears from node-i and -j at Hop-CNT=k and k+1, respectively. Then, node-s selects node-i, and transmits a packet to the node at Hop-CNT=k+1. Likewise, node-u discovers a route via node-s. Thus, nodes discover a route by overhearing neighbor communications, and establish a route via the neighboring nodes.

When an intermediate node (node-f) is required to relay a packet by a node (node-i), it forwards the packet to the next hop node according to the routing table. Concurrently, node-f records in its routing table the addresses of node-i and the source node (node-s) for the backward path. When a reply packet arrives at node-f to deliver to node-s, node-f recognizes to relay the packet to node-i according to the routing table. Likewise, node-i relays the packet to node-s.

In the event that an intermediate node (node-i) detects failure in forwarding a packet to the next hop node (node-f ), node-i replies an error message to the backward node (node-s) according to the routing table, then deletes the data of node-f from the routing table of nodei. In conditions where node-i does not have another next hop node information in the table, it returns expiration as the error message to node-s. If node-i has an alternative path in the routing table, it returns route-error instead of expiration to its backward node. When the route-error arrives at the source node, the node retransmits the packet to the same next hop node as alternative path. Then, node-i forwards the packet to the alternative node, node-g. When the source node receives expiration, it must select another next hop node. If there is no entry in the table of the source node, the source node has to hold on until it detects a new entry node by overhearing.

#### **4. Experimental system**

#### **4.1. Experimental centralized hierarchical network**

A dedicated data collection system was developed based on centralized hierarchical network‐ ing scheme for damage monitoring. The experimental system was designed tocollect datafrom lifeline facilities such as gas pipelines, water pipelines and sensing devices [28, 29]. The systemwas configured with two layers, to monitor the state of city lifelines of about 256,000 residences in a whole city.

**Figure 13.** A model of acentralized hierarchical damage monitoring system.

Provided that node-s detects multiple nodes which can reach BS, it records the nodes' address and Hop-CNT, and selects a node which has the shortest hopping range. If multiple nodes are found whichhave the shortest route, it may select one of them randomly. Provided that nodes notices it cannot overhear the communicationsfromthe node recorded in its routing table during the time to live (TTL), node-s decides the node is not available and deletes the record of the node in the table. The value of TTL is designated as a system parameter in advance.

42 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

Provided that a certain node transmits a packet, its Hop-CNT is set up at the value increment‐ ing the value of the next hop node. Thereby, the further neighboring nodes can overhear the communications and discover an available next hop node. Node-i is transmitting a packet to node-f at Hop-CNT=k. node-j is also transmitting at k+1. node-s overhears from node-i and -j at Hop-CNT=k and k+1, respectively. Then, node-s selects node-i, and transmits a packet to the node at Hop-CNT=k+1. Likewise, node-u discovers a route via node-s. Thus, nodes discover a route by overhearing neighbor communications, and establish a route via the

When an intermediate node (node-f) is required to relay a packet by a node (node-i), it forwards the packet to the next hop node according to the routing table. Concurrently, node-f records in its routing table the addresses of node-i and the source node (node-s) for the backward path. When a reply packet arrives at node-f to deliver to node-s, node-f recognizes to relay the packet

In the event that an intermediate node (node-i) detects failure in forwarding a packet to the next hop node (node-f ), node-i replies an error message to the backward node (node-s) according to the routing table, then deletes the data of node-f from the routing table of nodei. In conditions where node-i does not have another next hop node information in the table, it returns expiration as the error message to node-s. If node-i has an alternative path in the routing table, it returns route-error instead of expiration to its backward node. When the route-error arrives at the source node, the node retransmits the packet to the same next hop node as alternative path. Then, node-i forwards the packet to the alternative node, node-g. When the source node receives expiration, it must select another next hop node. If there is no entry in the table of the source node, the source node has to hold on until it detects a new entry node

A dedicated data collection system was developed based on centralized hierarchical network‐ ing scheme for damage monitoring. The experimental system was designed tocollect datafrom lifeline facilities such as gas pipelines, water pipelines and sensing devices [28, 29]. The systemwas configured with two layers, to monitor the state of city lifelines of about 256,000

to node-i according to the routing table. Likewise, node-i relays the packet to node-s.

neighboring nodes.

by overhearing.

**4. Experimental system**

residences in a whole city.

**4.1. Experimental centralized hierarchical network**

The experimental system comprises two-tier networks to cover a total of 250k terminals in an urban area of about 260 km2 (Fig. 13). The system employs 2.1 GHz radio communications for the upper layer and 2.4GHz CDMA forthe lower layer, and gathers data from gas meters, water meters and extra signals installed in houses to comprehend the conditions of lifeline facilities.

The upper layer network was designed to covers a whole citywith1024 cells.The central control station accesses the BS of each cell with a TDM (Time Division Multiplexing) wireless system. The upper layer radio system employed a narrow-band radio communications of 2.1GHz to connect CS and multiple BS, where the output power is 1 W, to cover a long distance. The air interface parameters are listed in Table 1. CS and BS of the upper layer contain a database server (Fig. 14):data transmitted from terminals to BS are stored in the database, and the server of BS provides data according to the requirement of CS. The CS stores the data in the database and provides the data in an application system.

The lower layer network consists of BS and multiple terminals in a cell, where the BS and terminals communicate in TD-CDMA at 2.4 GHz, combining CDMA and TDMA technologies. The output power is 10 mW or less in the communications range of 300 m. Though the experimental system was designed to contain 256 terminals in a cell, 128 terminals were actually installed in a residential area. The interface unit contained ina terminal is connected to a liquefied petroleum gas (LPG) meter, natural gas (NG) meter, water meter and additional signals (Fig. 14). Figure 15 shows the experimental CDMA and interface units connecting with meters.

Frame error rate (FER) is defined as the rate of communications failure in two-way transmis‐ sion. The experiment observed and recorded the number of communications failure in each of 128 terminals, as in Figure 16, where the FER is indicated on the average of 128 terminals. The average FER was approximately 1.5×10−3. Thereby, the experiment showed the CDMA system achieved a low FER on the order of 10−3 even in the output power of 10mW, where the communications range is within 300m.

Figure 14.Experimental system for lifeline monitoring. **Figure 14.** Experimental system for lifeline monitoring.

Fig. 17. **Experimentalunits.**

waytransmission.Theexperimentobservedandrecordedthenumberofcommunicationsfailureineach

inFigure16,wheretheFERisindicatedontheaverageof128terminals.TheaverageFERwasapproximately 1.5103.Thereby,theexperimentshowedtheCDMAsystemachievedalowFERontheorderof103eve

Fig. 18. **TimeseriesbehaviourofFERinCDMAchannels.**

Elapsed Time (hour)

0 8 16 24 32 40 48 56 64 72

Frameerrorrate(FER)isdefinedastherateofcommunicationsfailureintwo‐

nintheoutputpowerof10mW,wherethecommunicationsrangeiswithin300m.

0.0

2.0

4.0

6.0

*Frame Error Rate* (10-3)

8.0

10.0

indicated on the average of 128 terminals. The average FER was approximately 1.5103. Thereby, the experiment

0.0

**Figure 16.** Time series behaviour of FER in CDMA channels.

**Figure 17.** *FER*of each terminal

0 8 16 24 32 40 48 56 64 72

Multi-Tier Networks for Citywide Damage Monitoring in a Natural Disaster

http://dx.doi.org/10.5772/55513

45

Elapsed Time (hour)

The experimental CDMA system in the lower layer was designed to access 256 terminals in the interval of 10.24 seconds. The experiments confirmed that the system operates in 10.24sec‐ ondsfor256 terminals (128 actual terminals and 128 dummy terminals). On the other hand, the data rate in the upper layer was designed to transmit data at 288 kbit/s from BS to CS in the

2.0

4.0

*Frame Error Rate*(10


)

6.0

8.0

10.0

showed the CDMA system achieved a low FER on the order of 103 even in the output power of 10mW, where the communications range is within 300m. **Figure 15.** Experimental units.

of128terminals,as

**Figure 16.** Time series behaviour of FER in CDMA channels.

**Figure 17.** *FER*of each terminal

Figure 14.Experimental system for lifeline monitoring.

N-Gas Meter

42B 46B 44B

24B 19B 19B

<<CDMA Channels>>

LP-Gas Meter

**Figure 14.** Experimental system for lifeline monitoring.

Fw

Rv

CDMA Base

Interface Unit

Lifeline Data: 62B

TX/RX Applicatio <sup>n</sup> DB Serve

44 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

Central Control Station

Base Station

2.1 GHz

Database Server

> Water Meter

CDMA Terminal CDMA Unit

2.1 GHz TX/RX

Additional Signals

188B

<<2.1GHz >>

Upper Layer

Lower Layer

Figure 15.Experimental units.

communications range is within 300m.

nintheoutputpowerof10mW,wherethecommunicationsrangeiswithin300m.

0.0

2.0

4.0

6.0

*Frame Error Rate* (10-3)

8.0

10.0

Frameerrorrate(FER)isdefinedastherateofcommunicationsfailureintwo‐

of128terminals,as

**Figure 15.** Experimental units.

Fig. 17. **Experimentalunits.**

Water Meter

Gas Meter

Interface unit

CDMA unit

waytransmission.Theexperimentobservedandrecordedthenumberofcommunicationsfailureineach

inFigure16,wheretheFERisindicatedontheaverageof128terminals.TheaverageFERwasapproximately 1.5103.Thereby,theexperimentshowedtheCDMAsystemachievedalowFERontheorderof103eve

Fig. 18. **TimeseriesbehaviourofFERinCDMAchannels.**

Elapsed Time (hour)

0 8 16 24 32 40 48 56 64 72

Gas Meter

Water Meter

Interface unit

CDMA unit

Frame error rate (FER) is defined as the rate of communications failure in two-way transmission. The experiment observed and recorded the number of communications failure in each of 128 terminals, as in Figure 16, where the FER is indicated on the average of 128 terminals. The average FER was approximately 1.5103. Thereby, the experiment showed the CDMA system achieved a low FER on the order of 103 even in the output power of 10mW, where the

The experimental CDMA system in the lower layer was designed to access 256 terminals in the interval of 10.24 seconds. The experiments confirmed that the system operates in 10.24sec‐ ondsfor256 terminals (128 actual terminals and 128 dummy terminals). On the other hand, the data rate in the upper layer was designed to transmit data at 288 kbit/s from BS to CS in the polling mode. The experiments showed the data collection time from 1024 BS (three BS and 1021 dummy stations) to CS was 424 seconds in conditions where the data size from each BS is 5120 bytes, i.e. 20 bytes times 256 terminals. To acquire urgent information from every terminal immediately, the BS extracts the state information of two bytes from the data which was acquired in each terminal, and gathers the urgent data of 512 bytes in the cell. By trans‐ mitting 512-byte data to CS, the data collection time from 1024 BS was 51.7 seconds.

where mi is the number of nodes reachable to BS at i hops, and γ1, γ2,,, γn are the reachability

during T (seconds) to the amount of packets that all nodes can transmit in the network, and is

(*n*)) is the ratio of the number of transmitted packets

http://dx.doi.org/10.5772/55513

47

Multi-Tier Networks for Citywide Damage Monitoring in a Natural Disaster

<sup>=</sup> å <sup>×</sup> (3)

¯

1 () () *n i i*

 *n qT NT* =

(T) is the number of packets arriving at BS by i hops during T seconds.

Results are shown in Figure 18, in conditions where the radius of the cell is 340m, and the number of nodes is 901. In conditions where DCNR is 60% or higher, reachability at MR=2 is up to 98%. Even if DCNR is only 20%, it maintains reachability of approximately 90% within three hops. Figure 19 shows the throughput as a function of DCNR, in conditions where the cell size is 340m. Even if increasing MR at 1, 2, 3 and unlimited hopping, throughput is not

ResultsareshowninFigure18,inconditionswheretheradiusofthecellis340m,andthenumberofnodesis 901.InconditionswhereDCNRis60%orhigher,reachabilityatMR=2isupto98%.EvenifDCNRisonly20%,it maintainsreachabilityofapproximately90%withinthreehops.Figure19showsthethroughputasafuncti onofDCNR,inconditionswherethecellsizeis340m.EvenifincreasingMRat1,2,3andunlimitedhopping,t

Fig. 20. **ReachabilityforDCNR.**

*DCNR*

0.0 0.2 0.4 0.6 0.8 1.0

Fig. 21. **ThroughputforDCNR.**

*DCNR*

0.0 0.2 0.4 0.6 0.8 1.0

MR=1 MR=2 MR=3 MR=8

MR=1 MR=2 MR=3 MR=4

tiernetworksforcitywidedamagemonitoringinanaturaldisaster.Weshowedtheschemeofthecentraliz edhierarchicalnetworkandtheexperimentalsystemdesignedfordedicateddamagemonitoring.Theres

CDMAsystemachievedtheframeerrorrateontheorderof103evenintheoutputpowerof10mW,where thecommunicationsrangeiswithin300m.Themonitoringsystemiscapableofcollectinginformationwit hinoneminutefrom256,000terminalsdeployedinawholecity.Thereby,thesystemisusefulandeffective tocollectdataquicklyandstablyinconditionswherethelinkscouldbemaintained.Basedontheconceptof thecentralizedhierarchicalnetworkandtheexperimentalresults,weshowedthehybridwirelessmonito ringsystemenhancedwith*adhoc*networks.Theexperimentsbycomputersimulationshowedthenetwor

kiscapableofimprovingreachabilityofpacketseveninthedamageconditionsinanaturaldisaster.

h

in each hop count from one to n.

given by equation (3).

where qi

**5. CONCLUSION** 

Wediscussedaschemeofmulti‐

ultsshowedtheexperimentalTD‐

Average of throughput within n hops (*η*

improved drastically like reachability.

*Reachability* 

*Throughput* 

**Figure 18.** Reachability for DCNR.

0.0

0.2

0.4

0.6

0.8

1.0

0.0

0.2

0.4

0.6

0.8

1.0

hroughputisnotimproveddrasticallylikereachability.

whereqi(T)isthenumberofpacketsarrivingatBSbyihopsduringTseconds.

Thus, the system can collect a small data of two bytes from 256,000 terminals within 60 seconds though it takes about 7 minutes to collect a large size of data of 20 bytes from 256,000 terminals. Thereby, multi-tier centralized network is able tosurvey damage to lifeline in a whole city within one minute.

#### **4.2. Hybrid wireless monitoring enhanced with** *ad hoc* **networks**

The hybrid wireless network enhanced with *ad hoc* networks described in the previous section was evaluated by computer simulation, assuming a round shapecell the radius of which is denoted by r. BS is placed at the middle of the cell, and nodes are arranged in grid in a cell, where the grid interval is denoted by d.

Nodes for CH mode are selected randomly according to DCNR, which is defined later. Those nodes work as gateway nodes to relay packets from nodes. The rest of nodes operate in AD mode.

Assume that the communications range (l) of a node operating in AD mode is equal to the grid interval (d). Each node can access four adjacent nodes. This assumption is based on installing nodes in a residential area, which is arranged in grid. Assuming the distance between houses is 20m, the grid interval is 20m, and the communications range is also 20m.

Direct Connection Node Ratio (DCNR) is defined as the ratio of nodes, which can access BS directly, and is given by equation (1).

$$\text{DCNR} = \frac{\text{Number of direct connection nodes}}{\text{Total number of nodes in a cell}} = \frac{m\_i}{N} \tag{1}$$

where m1 is the number of nodes which can reach BS at one hop, and N is the number of all nodes in a cell.

Reachability (γ) is defined as the ratio of the nodes that are able to reach BS directly or by multihopping. The maximum hopping range (MR) is the upper limit of multi-hopping count (n). Reachability within n hops (γ (n)) is given by equation (2).

$$\gamma(n) = \sum\_{i=1}^{n} m\_i \int \mathbf{N} = \boldsymbol{\gamma}\_1 + \boldsymbol{\gamma}\_3 + \dots + \boldsymbol{\gamma}\_n \tag{2}$$

where mi is the number of nodes reachable to BS at i hops, and γ1, γ2,,, γn are the reachability in each hop count from one to n. ¯

Average of throughput within n hops (*η* (*n*)) is the ratio of the number of transmitted packets during T (seconds) to the amount of packets that all nodes can transmit in the network, and is given by equation (3).

$$\overline{\eta(n)} = \sum\_{i=1}^{n} q\_i(T) \Bigg/ \mathcal{N} \cdot T \tag{3}$$

where qi (T) is the number of packets arriving at BS by i hops during T seconds.

Results are shown in Figure 18, in conditions where the radius of the cell is 340m, and the number of nodes is 901. In conditions where DCNR is 60% or higher, reachability at MR=2 is up to 98%. Even if DCNR is only 20%, it maintains reachability of approximately 90% within three hops. Figure 19 shows the throughput as a function of DCNR, in conditions where the cell size is 340m. Even if increasing MR at 1, 2, 3 and unlimited hopping, throughput is not improved drastically like reachability. whereqi(T)isthenumberofpacketsarrivingatBSbyihopsduringTseconds. ResultsareshowninFigure18,inconditionswheretheradiusofthecellis340m,andthenumberofnodesis 901.InconditionswhereDCNRis60%orhigher,reachabilityatMR=2isupto98%.EvenifDCNRisonly20%,it

maintainsreachabilityofapproximately90%withinthreehops.Figure19showsthethroughputasafuncti onofDCNR,inconditionswherethecellsizeis340m.EvenifincreasingMRat1,2,3andunlimitedhopping,t

Fig. 20. **ReachabilityforDCNR.**

Fig. 21. **ThroughputforDCNR.**

*DCNR*

0.0 0.2 0.4 0.6 0.8 1.0

MR=1 MR=2 MR=3 MR=8

tiernetworksforcitywidedamagemonitoringinanaturaldisaster.Weshowedtheschemeofthecentraliz edhierarchicalnetworkandtheexperimentalsystemdesignedfordedicateddamagemonitoring.Theres

CDMAsystemachievedtheframeerrorrateontheorderof103evenintheoutputpowerof10mW,where thecommunicationsrangeiswithin300m.Themonitoringsystemiscapableofcollectinginformationwit hinoneminutefrom256,000terminalsdeployedinawholecity.Thereby,thesystemisusefulandeffective tocollectdataquicklyandstablyinconditionswherethelinkscouldbemaintained.Basedontheconceptof thecentralizedhierarchicalnetworkandtheexperimentalresults,weshowedthehybridwirelessmonito ringsystemenhancedwith*adhoc*networks.Theexperimentsbycomputersimulationshowedthenetwor

kiscapableofimprovingreachabilityofpacketseveninthedamageconditionsinanaturaldisaster.

1.0 **Figure 18.** Reachability for DCNR.

*Throughput* 

0.0

0.2

0.4

0.6

0.8

**5. CONCLUSION** 

Wediscussedaschemeofmulti‐

ultsshowedtheexperimentalTD‐

hroughputisnotimproveddrasticallylikereachability.

polling mode. The experiments showed the data collection time from 1024 BS (three BS and 1021 dummy stations) to CS was 424 seconds in conditions where the data size from each BS is 5120 bytes, i.e. 20 bytes times 256 terminals. To acquire urgent information from every terminal immediately, the BS extracts the state information of two bytes from the data which was acquired in each terminal, and gathers the urgent data of 512 bytes in the cell. By trans‐

Thus, the system can collect a small data of two bytes from 256,000 terminals within 60 seconds though it takes about 7 minutes to collect a large size of data of 20 bytes from 256,000 terminals. Thereby, multi-tier centralized network is able tosurvey damage to lifeline in a whole city

The hybrid wireless network enhanced with *ad hoc* networks described in the previous section was evaluated by computer simulation, assuming a round shapecell the radius of which is denoted by r. BS is placed at the middle of the cell, and nodes are arranged in grid in a cell,

Nodes for CH mode are selected randomly according to DCNR, which is defined later. Those nodes work as gateway nodes to relay packets from nodes. The rest of nodes operate in AD

Assume that the communications range (l) of a node operating in AD mode is equal to the grid interval (d). Each node can access four adjacent nodes. This assumption is based on installing nodes in a residential area, which is arranged in grid. Assuming the distance between houses

Direct Connection Node Ratio (DCNR) is defined as the ratio of nodes, which can access BS

Number of direct connection nodes <sup>1</sup> Total number of nodes in a cell

where m1 is the number of nodes which can reach BS at one hop, and N is the number of all

Reachability (γ) is defined as the ratio of the nodes that are able to reach BS directly or by multihopping. The maximum hopping range (MR) is the upper limit of multi-hopping count (n).

1 3

gg

*i n*

 g

= = + +×××+ å (2)

*m*

*N* = = (1)

is 20m, the grid interval is 20m, and the communications range is also 20m.

mitting 512-byte data to CS, the data collection time from 1024 BS was 51.7 seconds.

**4.2. Hybrid wireless monitoring enhanced with** *ad hoc* **networks**

46 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

within one minute.

mode.

nodes in a cell.

where the grid interval is denoted by d.

directly, and is given by equation (1).

*DCNR*

Reachability within n hops (γ (n)) is given by equation (2).

( )

g

1

*i*

*n*

*n mN*

=

0.0

0.2

0.4

0.6

0.8

1.0

whereqi(T)isthenumberofpacketsarrivingatBSbyihopsduringTseconds.

hroughputisnotimproveddrasticallylikereachability.

*Reachability* 

Fig. 21. **ThroughputforDCNR.**

Fig. 20. **ReachabilityforDCNR.**

0.0 0.2 0.4 0.6 0.8 1.0

MR=1 MR=2 MR=3 MR=4

**References**

Magazine, Vol. 44, No. 6, pp. 48-55.

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zine, vol.38, no.12, pp.134-142.

zine, Vol. 50, No. 2, pp. 116-71120.

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[2] Balachandran K, Budka KC, Chu TP, Doumi TL, and Kang JH, (2006). Mobile Res‐ ponder Communication Networks for Public Safety, IEEE Communications Maga‐

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working Conference (WCNC 2010), pp. 1-6, Sydney.

ResultsareshowninFigure18,inconditionswheretheradiusofthecellis340m,andthenumberofnodesis 901.InconditionswhereDCNRis60%orhigher,reachabilityatMR=2isupto98%.EvenifDCNRisonly20%,it maintainsreachabilityofapproximately90%withinthreehops.Figure19showsthethroughputasafuncti onofDCNR,inconditionswherethecellsizeis340m.EvenifincreasingMRat1,2,3andunlimitedhopping,t

**Figure 19.** Throughput for DCNR.

#### **5. CONCLUSION 5. Conclusion**

Wediscussedaschemeofmulti‐ tiernetworksforcitywidedamagemonitoringinanaturaldisaster.Weshowedtheschemeofthecentraliz edhierarchicalnetworkandtheexperimentalsystemdesignedfordedicateddamagemonitoring.Theres ultsshowedtheexperimentalTD‐ CDMAsystemachievedtheframeerrorrateontheorderof103evenintheoutputpowerof10mW,where thecommunicationsrangeiswithin300m.Themonitoringsystemiscapableofcollectinginformationwit hinoneminutefrom256,000terminalsdeployedinawholecity.Thereby,thesystemisusefulandeffective tocollectdataquicklyandstablyinconditionswherethelinkscouldbemaintained.Basedontheconceptof thecentralizedhierarchicalnetworkandtheexperimentalresults,weshowedthehybridwirelessmonito We discussed a scheme of multi-tier networks for citywide damage monitoring in a natural disaster. We showed the scheme of the centralized hierarchical network and the experimental system designed for dedicated damage monitoring. The results showed the experimental TD-CDMA system achieved the frame error rate on the order of 10−3 even in the output power of 10mW, where the communications range is within 300m. The monitoring system is capable of collecting information within one minute from 256,000 terminals deployed in a whole city. Thereby, the system is useful and effective to collect data quickly and stably in conditions where the links could be maintained. Based on the concept of the centralized hierarchical network and the experimental results, we showed the hybrid wireless monitoring system enhanced with *ad hoc* networks. The experiments by computer simulation showed the network is capable of improving reachability of packets even in the damage conditions in a natural disaster.

#### kiscapableofimprovingreachabilityofpacketseveninthedamageconditionsinanaturaldisaster. **Author details**

Takahiro Fujiwara1 and Takashi Watanabe2

1 Department of Computer Engineering, Hakodate National College of Technology, Hako‐ date, Japan

ringsystemenhancedwith*adhoc*networks.Theexperimentsbycomputersimulationshowedthenetwor

2 Graduate School of Science and Technology, Shizuoka University, Hamamatsu, Japan

#### **References**

whereqi(T)isthenumberofpacketsarrivingatBSbyihopsduringTseconds.

hroughputisnotimproveddrasticallylikereachability.

*Throughput* 

**Figure 19.** Throughput for DCNR.

**5. Conclusion**

0.0

0.2

0.4

0.6

0.8

1.0

0.0

48 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

0.2

0.4

0.6

0.8

1.0

*Reachability* 

**5. CONCLUSION** 

Wediscussedaschemeofmulti‐

ultsshowedtheexperimentalTD‐

disaster.

**Author details**

Takahiro Fujiwara1

date, Japan

ResultsareshowninFigure18,inconditionswheretheradiusofthecellis340m,andthenumberofnodesis 901.InconditionswhereDCNRis60%orhigher,reachabilityatMR=2isupto98%.EvenifDCNRisonly20%,it maintainsreachabilityofapproximately90%withinthreehops.Figure19showsthethroughputasafuncti onofDCNR,inconditionswherethecellsizeis340m.EvenifincreasingMRat1,2,3andunlimitedhopping,t

Fig. 20. **ReachabilityforDCNR.**

*DCNR*

0.0 0.2 0.4 0.6 0.8 1.0

Fig. 21. **ThroughputforDCNR.**

*DCNR*

0.0 0.2 0.4 0.6 0.8 1.0

MR=1 MR=2 MR=3 MR=8

MR=1 MR=2 MR=3 MR=4

tiernetworksforcitywidedamagemonitoringinanaturaldisaster.Weshowedtheschemeofthecentraliz edhierarchicalnetworkandtheexperimentalsystemdesignedfordedicateddamagemonitoring.Theres

We discussed a scheme of multi-tier networks for citywide damage monitoring in a natural disaster. We showed the scheme of the centralized hierarchical network and the experimental system designed for dedicated damage monitoring. The results showed the experimental TD-CDMA system achieved the frame error rate on the order of 10−3 even in the output power of 10mW, where the communications range is within 300m. The monitoring system is capable of collecting information within one minute from 256,000 terminals deployed in a whole city. Thereby, the system is useful and effective to collect data quickly and stably in conditions where the links could be maintained. Based on the concept of the centralized hierarchical network and the experimental results, we showed the hybrid wireless monitoring system enhanced with *ad hoc* networks. The experiments by computer simulation showed the network is capable of improving reachability of packets even in the damage conditions in a natural

CDMAsystemachievedtheframeerrorrateontheorderof103evenintheoutputpowerof10mW,where thecommunicationsrangeiswithin300m.Themonitoringsystemiscapableofcollectinginformationwit hinoneminutefrom256,000terminalsdeployedinawholecity.Thereby,thesystemisusefulandeffective tocollectdataquicklyandstablyinconditionswherethelinkscouldbemaintained.Basedontheconceptof thecentralizedhierarchicalnetworkandtheexperimentalresults,weshowedthehybridwirelessmonito ringsystemenhancedwith*adhoc*networks.Theexperimentsbycomputersimulationshowedthenetwor

kiscapableofimprovingreachabilityofpacketseveninthedamageconditionsinanaturaldisaster.

1 Department of Computer Engineering, Hakodate National College of Technology, Hako‐

2 Graduate School of Science and Technology, Shizuoka University, Hamamatsu, Japan

and Takashi Watanabe2


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Packet Data Networks, Proc. of IEEE GLOBECOM2002, Taipei.

System: iCAR, IEEE J-SAC vol.19, no.10, pp.2105-2115.

works, IEEE Computer Magazine, Vol. 37, No. 2, pp. 40- 46.

Communications Magazine, Vol. 50, No. 6, pp. 65-71.

50 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

TR 25.924, v1.0.0, December 1999.

Proc. of IEEE PIMRC2002, Lisbon.

pp.90-100, New Orleans.

COMM'94, pp.234-244, London.

99-6104.

demic.

62-68.

Stanford.


**Chapter 3**

**Impact of Hurricane Katrina on the Louisiana**

Additional information is available at the end of the chapter

William T. Robinson

**1. Introduction**

http://dx.doi.org/10.5772/55472

such as HIV/AIDS, may also be felt.

**HIV/AIDS Epidemic: A Socio-Ecological Perspective**

Emergency preparedness is an important issue and public health professionals seek to plan for and anticipate the effect of large scale disasters. The chief concern may be the impact of disasters on infection and infection control, or in some cases the impact of the public's health is a direct result of the disaster itself. For example cholera outbreaks such as seen in Haiti following the 2010 earthquake, or radiation sickness as a result of damage to the Fukushima plant. However, the effect of disasters on other epidemics, including more chronic diseases

Hurricane Katrina and the failure of the federal levee system remains one of the largest and costliest natural or man-made disasters in U.S. record. In all Katrina is estimated to have cost over 100 billion dollars in damage and recovery costs [1] with nearly 2000 people dead or presumed dead [2]. While Katrina had impacts across the Gulf South, the city and metropolitan area of New Orleans Louisiana sustained the most devastation, which resulted in a near total evacuation of the city that continues to be felt seven years later. Crouse-Quinn [3] have

Like the rest of the South, Louisiana and New Orleans have high concentrations of people living with HIV/AIDS as well as high rates of newly infected cases. In 2005 there were 21,062 persons living with HIV/AIDS in the Alabama, Mississippi and Louisiana Gulf Coast area [4] and 7068 people living with HIV/AIDS in the New Orleans metropolitan area [5]. According to the CDC, the state ranked 5th and the metropolitan area ranked 7th in new AIDS cases in

Thus the intersection of a disaster such as Katrina and the resultant long lasting effects of the storm and flood may have particular relevance to the large population living with HIV/AIDS

and reproduction in any medium, provided the original work is properly cited.

© 2013 Robinson; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

© 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

distribution, and reproduction in any medium, provided the original work is properly cited.

remarked that Katrina was both a social as well as a public health disaster.

2005, with 21.2 and 30.3 cases per 100,000 residents, respectively [6].

## **Impact of Hurricane Katrina on the Louisiana HIV/AIDS Epidemic: A Socio-Ecological Perspective**

William T. Robinson

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55472

### **1. Introduction**

Emergency preparedness is an important issue and public health professionals seek to plan for and anticipate the effect of large scale disasters. The chief concern may be the impact of disasters on infection and infection control, or in some cases the impact of the public's health is a direct result of the disaster itself. For example cholera outbreaks such as seen in Haiti following the 2010 earthquake, or radiation sickness as a result of damage to the Fukushima plant. However, the effect of disasters on other epidemics, including more chronic diseases such as HIV/AIDS, may also be felt.

Hurricane Katrina and the failure of the federal levee system remains one of the largest and costliest natural or man-made disasters in U.S. record. In all Katrina is estimated to have cost over 100 billion dollars in damage and recovery costs [1] with nearly 2000 people dead or presumed dead [2]. While Katrina had impacts across the Gulf South, the city and metropolitan area of New Orleans Louisiana sustained the most devastation, which resulted in a near total evacuation of the city that continues to be felt seven years later. Crouse-Quinn [3] have remarked that Katrina was both a social as well as a public health disaster.

Like the rest of the South, Louisiana and New Orleans have high concentrations of people living with HIV/AIDS as well as high rates of newly infected cases. In 2005 there were 21,062 persons living with HIV/AIDS in the Alabama, Mississippi and Louisiana Gulf Coast area [4] and 7068 people living with HIV/AIDS in the New Orleans metropolitan area [5]. According to the CDC, the state ranked 5th and the metropolitan area ranked 7th in new AIDS cases in 2005, with 21.2 and 30.3 cases per 100,000 residents, respectively [6].

Thus the intersection of a disaster such as Katrina and the resultant long lasting effects of the storm and flood may have particular relevance to the large population living with HIV/AIDS

© 2013 Robinson; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

or those who may be at risk for HIV in Louisiana. Given that in many ways this can be seen as a vulnerable population who may be disproportionately affected, it was critical to examine the impact of the storm on the epidemic.

One cannot, however, describe the impact of a single event such as Katrina on the entire epidemic without considering its effects on the individual as well as the various social and environmental contexts. It is increasingly recognized that explanations for determinants of health that operate solely on the individual level are inadequate [7]. Models of health and health behavior need to incorporate factors such as social and physical environments that nest individuals within these levels. The primary method of theorizing about health and health related behaviors from this multi-level framework has been the social-ecological approach [8, 9], that postulates a series of levels or strata at which these health-related risks and protective factors may operate. These strata often begin with the more proximal causes or moderators of disease, at the individual or intrapersonal level, and move towards the more distal interper‐ sonal risks, social and cultural factors and ultimately societal, structural or environmental level factors. Several researchers have pointed to the importance of the ecological framework or inclusion of these multilevel factors in understanding the HIV/AIDS epidemic [8,10-12].

to generate specific research questions and possible alternate hypotheses dealing with each of

Impact of Hurricane Katrina on the Louisiana HIV/AIDS epidemic: A Socio-Ecological Perspective

http://dx.doi.org/10.5772/55472

55

**Figure 2.** Logic tree conceptual diagram of potential Post Katina impact on the local HIV epidemic

Figure 2 represents the operationalization of these questions and the structure of their alternate hypotheses into a logic tree. These levels are explored in this chapter and the existing literature on HIV/AIDS and Hurricane Katrina is summarized and interpreted in this perspective.

At the individual level, the primary changes that an event such as Katrina might have on the HIV epidemic are those of individual behaviors among those with or at risk for HIV/AIDS, and the impact that the disaster itself may have on the disease status of the individual living

The literature has pointed to changes in risk behaviors following natural and man-made disasters. While many of these changes may be moderated by the context of the event, increases in HIV specific risk behaviors, such as sexual or substance using practice, are common. Several

studies specific to New Orleans have confirmed this effect after Katrina.

these levels.

**2. Individual level**

with HIV/AIDS.

**2.1. Behavioral impact**

Figure 1 presents an adaptation of the social-ecological model as one possible representation of levels of risk for HIV/AIDS. Following this model, Katrina might impact the way in which HIV acts at the individual level, such as influencing individual risk behaviors with unsafe sex or substance use practices. Social and interpersonal factors might be have been influenced by disruptions of networks or neighborhoods, and structural or policy level changes may have occurred at the system level such as the health care infrastructure.

Each of these levels embodies multiple research questions, often with multiple alternate hypothesized results. In order to best capture the possible ways in which the epidemic may have been affected by Katrina, the method of Strong Inference outlined by Platt [13] was used Impact of Hurricane Katrina on the Louisiana HIV/AIDS epidemic: A Socio-Ecological Perspective http://dx.doi.org/10.5772/55472 55

**Figure 2.** Logic tree conceptual diagram of potential Post Katina impact on the local HIV epidemic

to generate specific research questions and possible alternate hypotheses dealing with each of these levels.

Figure 2 represents the operationalization of these questions and the structure of their alternate hypotheses into a logic tree. These levels are explored in this chapter and the existing literature on HIV/AIDS and Hurricane Katrina is summarized and interpreted in this perspective.

#### **2. Individual level**

or those who may be at risk for HIV in Louisiana. Given that in many ways this can be seen as a vulnerable population who may be disproportionately affected, it was critical to examine

One cannot, however, describe the impact of a single event such as Katrina on the entire epidemic without considering its effects on the individual as well as the various social and environmental contexts. It is increasingly recognized that explanations for determinants of health that operate solely on the individual level are inadequate [7]. Models of health and health behavior need to incorporate factors such as social and physical environments that nest individuals within these levels. The primary method of theorizing about health and health related behaviors from this multi-level framework has been the social-ecological approach [8, 9], that postulates a series of levels or strata at which these health-related risks and protective factors may operate. These strata often begin with the more proximal causes or moderators of disease, at the individual or intrapersonal level, and move towards the more distal interper‐ sonal risks, social and cultural factors and ultimately societal, structural or environmental level factors. Several researchers have pointed to the importance of the ecological framework or inclusion of these multilevel factors in understanding the HIV/AIDS epidemic [8,10-12].

Figure 1 presents an adaptation of the social-ecological model as one possible representation of levels of risk for HIV/AIDS. Following this model, Katrina might impact the way in which HIV acts at the individual level, such as influencing individual risk behaviors with unsafe sex or substance use practices. Social and interpersonal factors might be have been influenced by disruptions of networks or neighborhoods, and structural or policy level changes may have

Each of these levels embodies multiple research questions, often with multiple alternate hypothesized results. In order to best capture the possible ways in which the epidemic may have been affected by Katrina, the method of Strong Inference outlined by Platt [13] was used

occurred at the system level such as the health care infrastructure.

the impact of the storm on the epidemic.

54 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**Figure 1.** Socio Ecological Model of Health

At the individual level, the primary changes that an event such as Katrina might have on the HIV epidemic are those of individual behaviors among those with or at risk for HIV/AIDS, and the impact that the disaster itself may have on the disease status of the individual living with HIV/AIDS.

#### **2.1. Behavioral impact**

The literature has pointed to changes in risk behaviors following natural and man-made disasters. While many of these changes may be moderated by the context of the event, increases in HIV specific risk behaviors, such as sexual or substance using practice, are common. Several studies specific to New Orleans have confirmed this effect after Katrina.

Morse [14] followed up with an existing cohort of New Orleans injection drug users 5 months after Katrina: 60% expressed that their risk behaviors had increased in the time since Katrina. Furthermore, among the injection drug users that had evacuated to other areas many had been incarcerated in other cities or had little or no difficulty in obtaining drugs. Kissinger [15] recontacted a small sample of young women who had accessed clinical reproductive health services before the storm and found that many of them had stopped using birth control, contracted sexually transmitted infections or gotten pregnant after the storm. Similarly, she found high rates of risk behaviors among a sample of Latino migrant workers who had newly arrived after the storm [16].

Post-traumatic stress disorder (PTSD) is a chronic and long lasting stress related condition that is often triggered by specific psychological trauma. PTSD is common in post disaster environ‐ ments and after Katrina, the entire population of New Orleans experienced trauma and levels of PTSD were shown to be highly prevalent in the city across many groups [18, 19] Wagner et al. [20] provide a synopsis of potential for PTSD, substance use, and HIV risk among youth subsequent to Katrina. Among persons living with HIV/AIDS, high levels of PTSD have been also shown following the experience of notification of a positive HIV diagnosis [21], and this

Impact of Hurricane Katrina on the Louisiana HIV/AIDS epidemic: A Socio-Ecological Perspective

http://dx.doi.org/10.5772/55472

57

In 2009, Reilly et al. [22] demonstrated high levels of PTSD (37%) in a sample of 145 persons living with HIV/AIDS in New Orleans one year after the storm. While high, these levels were consistent with other studies of the general population. By comparing the health status of those persons living with HIV/AIDS with PTSD to those who did not have clinical levels of stress, they found that persons living with HIV/AIDS with PTSD were less likely to have nondetectable viral load levels and were more likely to have weakened immune systems (CD4 counts) also two years after the storm. Thus, the disease status of the PTSD group was significantly worse than that of the group who showed low PTSD. It should be noted that this study was conducted on a clinic sample of persons living with HIV/AIDS who had already returned to and sought care in New Orleans, thus did not include people who were unable or

Disease status of persons living with HIV/AIDS was examined to detect differences in the between Katrina evacuees who had returned to New Orleans and those who remained displaced outside the metropolitan area [23]. Laboratory records reported to the state Office of Public Health for 18 months prior to the storm and 18 months post storm were obtained and coded according to current residential status. It was found that those persons living with HIV/ AIDS who had returned had overall CD4 counts comparable to residents from parts of the state unaffected by Katrina. Conversely, those persons living with HIV/AIDS who remained away from their homes showed both lower overall CD4 counts before and after the storm as well as showing a significantly greater decrease in CD4 in the time before to the time after the storm (Figure 3). While CD4 was used as the primary indicator of disease status in this study, similar results were found but not reported for increases in viral load laboratory results.

Other outstanding questions remain about the way in which an event such as Katrina might impact the disease progression of persons living with HIV/AIDS. While these increases in viral load and decreases in CD4 were found to be statistically significant 18 months out from the storm, studies are needed to examine the potential for longer term changes in these indicators. Furthermore, the extent to which these clinical indicators of disease status translate into other disease related outcomes is unknown. For example, do persons living with HIV/AIDS who were displaced or impacted by Katrina show shorter survival times from AIDS diagnoses to death? Similarly, reductions in time from HIV diagnosis to an AIDS diagnosis or AIDS

While stress, potentially in the form of PTSD, may be one explanation for these effects on disease status, other possible explanations are conceivable. For example, disruptions in medication regimen or adherence could also explain these results. This may be an intractable

diagnosing condition have not been established or investigated.

has adverse impact on the disease progression.

unwilling to return.

Since 2003, New Orleans has participated in the *National HIV Behavioral Surveillance System* funded by the Centers for Disease Control and Prevention, an annual survey that assesses HIV risk behaviors and access to testing and prevention services among the three populations at highest risk for HIV a) men who have sex with men, b) injection drug users and c) heterosexuals living in areas at high risk for HIV and poverty. From 2006 to 2009, samples from over 500 respondents in each of these high risk groups were asked to self-report on their perceived change in HIV risk during the 12 month immediately after the storm. Overall, self-reports of increased risk - from 60% to 70% - were more commonly reported than reports of decreased risk (15%-20%). The most commonly reported reasons for why an individual's risk might have increased included increased or additional sexual partners, unsafe practices and increases in substance use or use of injection drugs and unprotected sexual practices. The most common reported reason for why a person's HIV risk might have decreases was a decreased number of sex partners.

These results cannot demonstrate a causal relationship between Katrina and change in risk. Furthermore they are self-report data and may be subject to recall or social desirability bias. However, they still do point to the fact that HIV risk behavior increased among many individuals in the time after Katrina.

#### **2.2. Health status**

The action of HIV is to compromise the human immune system by attacking the types of white blood cells (called CD4) that fight off many types of infection. Today, the primary disease management tool for HIV is the use of drugs including highly active antiretroviral therapy. Medication adherence, however, is critical in order to adequately manage the disease. Persons living with HIV on these regimens are able to live much longer and often control the disease to the point where its viral load is undetectable in the bloodstream. As a result the immune system may be less compromised and higher CD4 counts (a typical measure of the immune system t-helper cells) may be seen.

The impact of stress on the human immune response has been well documented [17]. Chronic stress can lead to reduction in the immune system's ability to fight off infection. Given that the mechanism for HIV is its effect on the immune system itself, it is no surprise that studies have demonstrated the relationship between stressful life events and the disease status of persons living with HIV/AIDS.

Post-traumatic stress disorder (PTSD) is a chronic and long lasting stress related condition that is often triggered by specific psychological trauma. PTSD is common in post disaster environ‐ ments and after Katrina, the entire population of New Orleans experienced trauma and levels of PTSD were shown to be highly prevalent in the city across many groups [18, 19] Wagner et al. [20] provide a synopsis of potential for PTSD, substance use, and HIV risk among youth subsequent to Katrina. Among persons living with HIV/AIDS, high levels of PTSD have been also shown following the experience of notification of a positive HIV diagnosis [21], and this has adverse impact on the disease progression.

Morse [14] followed up with an existing cohort of New Orleans injection drug users 5 months after Katrina: 60% expressed that their risk behaviors had increased in the time since Katrina. Furthermore, among the injection drug users that had evacuated to other areas many had been incarcerated in other cities or had little or no difficulty in obtaining drugs. Kissinger [15] recontacted a small sample of young women who had accessed clinical reproductive health services before the storm and found that many of them had stopped using birth control, contracted sexually transmitted infections or gotten pregnant after the storm. Similarly, she found high rates of risk behaviors among a sample of Latino migrant workers who had newly

56 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

Since 2003, New Orleans has participated in the *National HIV Behavioral Surveillance System* funded by the Centers for Disease Control and Prevention, an annual survey that assesses HIV risk behaviors and access to testing and prevention services among the three populations at highest risk for HIV a) men who have sex with men, b) injection drug users and c) heterosexuals living in areas at high risk for HIV and poverty. From 2006 to 2009, samples from over 500 respondents in each of these high risk groups were asked to self-report on their perceived change in HIV risk during the 12 month immediately after the storm. Overall, self-reports of increased risk - from 60% to 70% - were more commonly reported than reports of decreased risk (15%-20%). The most commonly reported reasons for why an individual's risk might have increased included increased or additional sexual partners, unsafe practices and increases in substance use or use of injection drugs and unprotected sexual practices. The most common reported reason for why a person's HIV risk might have decreases was a decreased number

These results cannot demonstrate a causal relationship between Katrina and change in risk. Furthermore they are self-report data and may be subject to recall or social desirability bias. However, they still do point to the fact that HIV risk behavior increased among many

The action of HIV is to compromise the human immune system by attacking the types of white blood cells (called CD4) that fight off many types of infection. Today, the primary disease management tool for HIV is the use of drugs including highly active antiretroviral therapy. Medication adherence, however, is critical in order to adequately manage the disease. Persons living with HIV on these regimens are able to live much longer and often control the disease to the point where its viral load is undetectable in the bloodstream. As a result the immune system may be less compromised and higher CD4 counts (a typical measure of the immune

The impact of stress on the human immune response has been well documented [17]. Chronic stress can lead to reduction in the immune system's ability to fight off infection. Given that the mechanism for HIV is its effect on the immune system itself, it is no surprise that studies have demonstrated the relationship between stressful life events and the disease status of persons

arrived after the storm [16].

of sex partners.

**2.2. Health status**

individuals in the time after Katrina.

system t-helper cells) may be seen.

living with HIV/AIDS.

In 2009, Reilly et al. [22] demonstrated high levels of PTSD (37%) in a sample of 145 persons living with HIV/AIDS in New Orleans one year after the storm. While high, these levels were consistent with other studies of the general population. By comparing the health status of those persons living with HIV/AIDS with PTSD to those who did not have clinical levels of stress, they found that persons living with HIV/AIDS with PTSD were less likely to have nondetectable viral load levels and were more likely to have weakened immune systems (CD4 counts) also two years after the storm. Thus, the disease status of the PTSD group was significantly worse than that of the group who showed low PTSD. It should be noted that this study was conducted on a clinic sample of persons living with HIV/AIDS who had already returned to and sought care in New Orleans, thus did not include people who were unable or unwilling to return.

Disease status of persons living with HIV/AIDS was examined to detect differences in the between Katrina evacuees who had returned to New Orleans and those who remained displaced outside the metropolitan area [23]. Laboratory records reported to the state Office of Public Health for 18 months prior to the storm and 18 months post storm were obtained and coded according to current residential status. It was found that those persons living with HIV/ AIDS who had returned had overall CD4 counts comparable to residents from parts of the state unaffected by Katrina. Conversely, those persons living with HIV/AIDS who remained away from their homes showed both lower overall CD4 counts before and after the storm as well as showing a significantly greater decrease in CD4 in the time before to the time after the storm (Figure 3). While CD4 was used as the primary indicator of disease status in this study, similar results were found but not reported for increases in viral load laboratory results.

Other outstanding questions remain about the way in which an event such as Katrina might impact the disease progression of persons living with HIV/AIDS. While these increases in viral load and decreases in CD4 were found to be statistically significant 18 months out from the storm, studies are needed to examine the potential for longer term changes in these indicators. Furthermore, the extent to which these clinical indicators of disease status translate into other disease related outcomes is unknown. For example, do persons living with HIV/AIDS who were displaced or impacted by Katrina show shorter survival times from AIDS diagnoses to death? Similarly, reductions in time from HIV diagnosis to an AIDS diagnosis or AIDS diagnosing condition have not been established or investigated.

While stress, potentially in the form of PTSD, may be one explanation for these effects on disease status, other possible explanations are conceivable. For example, disruptions in medication regimen or adherence could also explain these results. This may be an intractable

Earthquake, may lead to significant population migration. In these cases, accurate data on current population estimates are critical to conduct public health and other important planning efforts, however, these data are often invalid or not available, such as in the case with Katrina.

Impact of Hurricane Katrina on the Louisiana HIV/AIDS epidemic: A Socio-Ecological Perspective

http://dx.doi.org/10.5772/55472

59

Because of the lack of availability of this important information in the absence of the traditional measures of population (e.g. the US Census), several attempts were made to calculate estimates of the return of the general population of New Orleans and the surrounding areas. One of the first comprehensive published results was the New Orleans Emergency Operations Center's Rapid Population Estimate Project, a survey realized by CDC and the Census [29]. Later survey estimates based on neighborhood enumerations [30] or made available from commercial sources or marketing research firms such as Claritas/Neilsen were released [31]. Eventually the U.S. Census was able to provide standard mid-year estimates of the city population, however, these figures have been regularly disputed by state and city officials and annually

Several problems and caveats with these data sources exist that make it very difficult to plan and conduct regular public health activities. Clearly the results of these studies are extremely time dependent especially given the rapid and ever changing pace of the population migra‐ tions in the months and years since Katrina. The results also lack a needed level of geographic specificity or resolution, that is to say they are often only available at the level of the entire parish. This is problematic when assessing smaller areas such as ZIP codes, neighborhoods or census blocks. Given the differences in damage to neighborhoods may differentially influence a person's ability to return to their home. Related to this is the disparity and disproportionate impact of Katrina on different racial, ethnic and socioeconomic groups. Only with the release of the complete 2010 Census will true and accurate data be available at the level of detail that

Because of the absence of reliable population data, planning efforts had to be based on nonempirical or proxy measures for traditional data. For example postal service measures based on the proportion of households within a ZIP code or neighborhood who were receiving mail were used in some cases. Greater New Orleans Data Center [32] conducted regular estimates using these sorts of methods. Other efforts included an ethnographic mixed methods approach that was conducted as part of the National HIV Behavioral Surveillance System. This project involved both qualitative and quantitative descriptions of neighborhoods that were identified as high risk for HIV prior to Katrina, which documented the potential viability of survey research within those areas. These efforts targeted neighborhoods of greatest HIV risk based

These investigations included systematic social observations or windshield surveys followed by brief street interviews, focus groups and semi structured interviews with neighborhood residents. In all staff rated neighborhoods in terms of the appropriateness for survey activities and overall recovery based on these measures, which included over 16,000 direct observations of individual residential units and over 100 interviews with neighborhood residents [32]. Many

is needed to conduct public health and planning activities.

**3.1. Population data**

amended.

on pre-storm data.

**Figure 3.** Average CD4 Counts Pre and Post Katrina for Evacuees, Returnees and Other state residents.

problem, however, given that PTSD itself has been shown to result in poor adherence [24,25]. Increases in viral load and decreased immune response at the individual level are certainly unfortunate, more problematic, however, may be the impact that this has on the epidemic itself. Recent studies have shown that individuals with high uncontrolled viral load, including those who are newly infected and may not know their status, are more likely to transmit the infection [26,27] and disruptions in medication can result in emergence of drug-resistant strains of the virus. Thus Katrina may have had a synergistic effect leading to increased risk behaviors and increased viral loads ultimately leading to increases in infections.

#### **3. Social network and geographic levels**

By far the most dramatic and long lasting effects of Katrina and the resulting failure of the federal levee systems was the widespread flooding and devastation of the city of New Orleans and surrounding communities. Many large areas of the city remained under over 10 feet of water for weeks after the storm. Furthermore, the city itself remained under a mandatory evacuation order with no critical services such as water or electricity for over a month until the New Orleans Mayor allowed a staged return to certain ZIP codes based on damage. Thus, people were unable to return to their, even undamaged, homes for long periods and those with significant damage were forced to relocate to other neighborhoods, cities or even states. Other large scale disasters may necessitate long or short term evacuation events. Flooding, radiation release, or damage, such as that observed in Katrina, Fukushima [28] or the 2010 Haiti Earthquake, may lead to significant population migration. In these cases, accurate data on current population estimates are critical to conduct public health and other important planning efforts, however, these data are often invalid or not available, such as in the case with Katrina.

#### **3.1. Population data**

problem, however, given that PTSD itself has been shown to result in poor adherence [24,25]. Increases in viral load and decreased immune response at the individual level are certainly unfortunate, more problematic, however, may be the impact that this has on the epidemic itself. Recent studies have shown that individuals with high uncontrolled viral load, including those who are newly infected and may not know their status, are more likely to transmit the infection [26,27] and disruptions in medication can result in emergence of drug-resistant strains of the virus. Thus Katrina may have had a synergistic effect leading to increased risk

By far the most dramatic and long lasting effects of Katrina and the resulting failure of the federal levee systems was the widespread flooding and devastation of the city of New Orleans and surrounding communities. Many large areas of the city remained under over 10 feet of water for weeks after the storm. Furthermore, the city itself remained under a mandatory evacuation order with no critical services such as water or electricity for over a month until the New Orleans Mayor allowed a staged return to certain ZIP codes based on damage. Thus, people were unable to return to their, even undamaged, homes for long periods and those with significant damage were forced to relocate to other neighborhoods, cities or even states. Other large scale disasters may necessitate long or short term evacuation events. Flooding, radiation release, or damage, such as that observed in Katrina, Fukushima [28] or the 2010 Haiti

behaviors and increased viral loads ultimately leading to increases in infections.

**Figure 3.** Average CD4 Counts Pre and Post Katrina for Evacuees, Returnees and Other state residents.

58 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**3. Social network and geographic levels**

Because of the lack of availability of this important information in the absence of the traditional measures of population (e.g. the US Census), several attempts were made to calculate estimates of the return of the general population of New Orleans and the surrounding areas. One of the first comprehensive published results was the New Orleans Emergency Operations Center's Rapid Population Estimate Project, a survey realized by CDC and the Census [29]. Later survey estimates based on neighborhood enumerations [30] or made available from commercial sources or marketing research firms such as Claritas/Neilsen were released [31]. Eventually the U.S. Census was able to provide standard mid-year estimates of the city population, however, these figures have been regularly disputed by state and city officials and annually amended.

Several problems and caveats with these data sources exist that make it very difficult to plan and conduct regular public health activities. Clearly the results of these studies are extremely time dependent especially given the rapid and ever changing pace of the population migra‐ tions in the months and years since Katrina. The results also lack a needed level of geographic specificity or resolution, that is to say they are often only available at the level of the entire parish. This is problematic when assessing smaller areas such as ZIP codes, neighborhoods or census blocks. Given the differences in damage to neighborhoods may differentially influence a person's ability to return to their home. Related to this is the disparity and disproportionate impact of Katrina on different racial, ethnic and socioeconomic groups. Only with the release of the complete 2010 Census will true and accurate data be available at the level of detail that is needed to conduct public health and planning activities.

Because of the absence of reliable population data, planning efforts had to be based on nonempirical or proxy measures for traditional data. For example postal service measures based on the proportion of households within a ZIP code or neighborhood who were receiving mail were used in some cases. Greater New Orleans Data Center [32] conducted regular estimates using these sorts of methods. Other efforts included an ethnographic mixed methods approach that was conducted as part of the National HIV Behavioral Surveillance System. This project involved both qualitative and quantitative descriptions of neighborhoods that were identified as high risk for HIV prior to Katrina, which documented the potential viability of survey research within those areas. These efforts targeted neighborhoods of greatest HIV risk based on pre-storm data.

These investigations included systematic social observations or windshield surveys followed by brief street interviews, focus groups and semi structured interviews with neighborhood residents. In all staff rated neighborhoods in terms of the appropriateness for survey activities and overall recovery based on these measures, which included over 16,000 direct observations of individual residential units and over 100 interviews with neighborhood residents [32]. Many of these areas were among the most heavily damaged neighborhoods and some continued to be classified as non-livable months or years post-Katrina.

**Figure 4.** Katrina damage in Lower 9th Ward

Figure 4 and Figure 5 document damage to structures in the devastated Lower 9th Ward that was not atypical of the area. Other areas, however, such as the French Quarter and Uptown, remained relatively untouched and showed denser occupation than pre-Katrina levels.

to assess the stage of an epidemic, it is also the key measure for planning for services to persons

Impact of Hurricane Katrina on the Louisiana HIV/AIDS epidemic: A Socio-Ecological Perspective

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61

For diseases such as HIV/AIDS, monitoring of local prevalence is often conducted by state or jurisdictional health departments through the use of registry or sentinel surveillance systems. Typical disease surveillance systems utilize either active or passive disease reporting of notifiable (legally reportable) conditions. In *active* systems the information usually is the result of field investigation conducted by epidemiologists or other trained staff. For example when an outbreak of foodborne illness triggers an active investigation of local restaurants. On the contrary, *passive* systems rely on information that is reported by medical providers or labora‐ tories. In the U.S. HIV/AIDS, surveillance is conducted using the enhanced HIV/AIDS Reporting System, which is for the most part a passive system. Address at diagnosis is often initially reported and in many cases it is rarely updated. Because of this, calculation of geographic disease burden or local prevalence is based on this address at diagnosis, rather

Under large scale migration or evacuation events, passive systems will *overestimate* the prevalence of the disease or condition because the system will not be updated: the address that was reported at the time of a person's HIV diagnosis will remain in place even after that person has evacuated. Two methods were developed to estimate the post-Katrina prevalence of persons living with HIV/AIDS [5] in the New Orleans region. One method, based on available population return data described above, applied the point estimates of the proportion of the general population to the number of pre-Katrina persons living with HIV/AIDS to compute estimated prevalence. A second method utilized available information from additional

living with the disease or prevention interventions for those at risk.

than last known address of the individual.

**Figure 5.** Katrina damage in Lower 9th Ward

Regardless of damage, the results of the qualitative and quantitative investigations showed clear disruption to peoples social and sexual network due to the changes in post-Katrina neighborhood level population. Interview results frequently referred to the splitting up of families, friends and social groups. In Brumsfa's Sociology of Katrina [33] the impact of the formal and informal social network is frequently mentioned. Under the socio-ecological model these are important potentially protective factors when one considers the potential roles of collective self-efficacy or the ability of a community to mobilize resources. When these ties are broken, the community suffers. These changes could represent possible risk factors as new, potentially HIV infected, partners enter fresh networks, or alternatively these disruptions could reduce the protective factors that social support has on decision making ability.

#### **3.2. Prevalence**

A more direct measure of community or geographic risk is the prevalence of disease within the areas that these networks are embedded within. Disease *prevalence* is often defined as the proportion of the population who have the disease or condition. While prevalence may be used Impact of Hurricane Katrina on the Louisiana HIV/AIDS epidemic: A Socio-Ecological Perspective http://dx.doi.org/10.5772/55472 61

**Figure 5.** Katrina damage in Lower 9th Ward

of these areas were among the most heavily damaged neighborhoods and some continued to

Figure 4 and Figure 5 document damage to structures in the devastated Lower 9th Ward that was not atypical of the area. Other areas, however, such as the French Quarter and Uptown, remained relatively untouched and showed denser occupation than pre-Katrina levels.

Regardless of damage, the results of the qualitative and quantitative investigations showed clear disruption to peoples social and sexual network due to the changes in post-Katrina neighborhood level population. Interview results frequently referred to the splitting up of families, friends and social groups. In Brumsfa's Sociology of Katrina [33] the impact of the formal and informal social network is frequently mentioned. Under the socio-ecological model these are important potentially protective factors when one considers the potential roles of collective self-efficacy or the ability of a community to mobilize resources. When these ties are broken, the community suffers. These changes could represent possible risk factors as new, potentially HIV infected, partners enter fresh networks, or alternatively these disruptions could reduce the protective factors that social support has on decision making ability.

A more direct measure of community or geographic risk is the prevalence of disease within the areas that these networks are embedded within. Disease *prevalence* is often defined as the proportion of the population who have the disease or condition. While prevalence may be used

be classified as non-livable months or years post-Katrina.

60 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**Figure 4.** Katrina damage in Lower 9th Ward

**3.2. Prevalence**

to assess the stage of an epidemic, it is also the key measure for planning for services to persons living with the disease or prevention interventions for those at risk.

For diseases such as HIV/AIDS, monitoring of local prevalence is often conducted by state or jurisdictional health departments through the use of registry or sentinel surveillance systems. Typical disease surveillance systems utilize either active or passive disease reporting of notifiable (legally reportable) conditions. In *active* systems the information usually is the result of field investigation conducted by epidemiologists or other trained staff. For example when an outbreak of foodborne illness triggers an active investigation of local restaurants. On the contrary, *passive* systems rely on information that is reported by medical providers or labora‐ tories. In the U.S. HIV/AIDS, surveillance is conducted using the enhanced HIV/AIDS Reporting System, which is for the most part a passive system. Address at diagnosis is often initially reported and in many cases it is rarely updated. Because of this, calculation of geographic disease burden or local prevalence is based on this address at diagnosis, rather than last known address of the individual.

Under large scale migration or evacuation events, passive systems will *overestimate* the prevalence of the disease or condition because the system will not be updated: the address that was reported at the time of a person's HIV diagnosis will remain in place even after that person has evacuated. Two methods were developed to estimate the post-Katrina prevalence of persons living with HIV/AIDS [5] in the New Orleans region. One method, based on available population return data described above, applied the point estimates of the proportion of the general population to the number of pre-Katrina persons living with HIV/AIDS to compute estimated prevalence. A second method utilized available information from additional secondary active surveillance or reporting systems, such as laboratory reports, to document the return or relocation of persons living with HIV/AIDS. Cases with any available information were used as a sample of all cases to impute return of all persons living with HIV/AIDS. Both of these methods were recomputed at regular intervals in order to inform and direct the state and local health department during this critical period.

Under normal circumstances, these two numbers are equal and estimates of the # of persons living in an area at mid-year (as provided by the US Census) are equal to the actual number of person years at risk because any change over the course of the year is assumed to be consistent and therefore the midpoint reflects an average. However, in cases where the change in population is sudden this assumption is no longer valid and the mid-year no longer represents a true reflection of person years. It should be expected therefore that the mid-year estimate for New Orleans might violate this assumption since the entire city was under a mandatory evacuation order and only allowed a staggered and slow return to their homes.

Impact of Hurricane Katrina on the Louisiana HIV/AIDS epidemic: A Socio-Ecological Perspective

http://dx.doi.org/10.5772/55472

63

Under large scale evacuation events, or other instances where the census does not accurately reflect the number of person years, disease rates will be drastically *underestimated*. Van Landingham [34,35] explained this phenomenon as applied to New Orleans murder rate. Due to the large population loss in the last months of 2005, there was an apparent drop in murders that was actually an artifact of calculations. After applying a corrected population estimate of the average person years at risk as a denominator the actual rate was similar to previous years.

Rates of HIV/AIDS calculated as those murder rates can produce incorrect estimates. In 2008 Robinson et al. [36] applied corrected estimates of the person years at risk to New Orleans HIV/ AIDS diagnosis data and found that there was a dramatic spike in disease diagnosis rates in

We have already discussed how some persons living with HIV/AIDS and persons at risk may have had some difficulty in accessing needed programs services such as family planning or reproductive health needs [15] one year following the storm. Many of these interventions may have been traditionally sought at local public clinics, many of which remained closed until well after this time. This would be an example of one structural factor that could influence the epidemic as a result of Katrina. Thus, Katrina influenced the epidemic at a policy or structural level to the extent that clinic closures acted as a barrier to utilization of family planning services or other reproductive health needs that could've been used in the prevention of HIV or

Clearly one major concern following a natural disaster is in maintaining the infrastructure of the health care system. For those impacted with a disease such as HIV this is of vital importance and disruptions to the system can mean fluctuations in the delivery and availability of badly needed drugs or access to drug supplemental assistance programs. Several reports document‐ ed the recovery of the health care system and health care providers such as the Medical Center of Louisiana of New Orleans. One year after Katrina over 50% of professionals surveyed from the American College of Emergency Providers reported very little or no progress in the emergency care system [37]. Though improvements were marked and continue to improve to this day, a great deal of uncertainty existed well past that time [18] including the question of

the future of the State's safety net health care system for indigent care.

the year following Katrina (Figure 7).

**4. Structural and policy level**

unplanned pregnancies.

Figure 6 shows the estimated return of *persons living with HIV/AIDS* (PLWH/A) to Orleans Parish at time intervals consistent with the available population data. The rate of return of persons living with HIV/AIDS, using the secondary surveillance estimates, consistently matches the return of the general population. Not shown here, however, are the group specific estimates, which again point to disproportionate return based on race, sex, and geographic area of residence. Surprisingly, mode of transmission was an important factor in the ability to return, with men who have sex with men returning much earlier and at higher rates. However, this may be confounded with the geographic and socioeconomic characteristics of where many of them resided.

#### **3.3. Incidence**

Disease rate is often calculated as the number of disease cases divided by the number of persons in the population. However, more formal definitions would introduce time and replace the number of persons in the population with number of person-years at risk in the population.

**Figure 6.** Estimated numbers of returning persons living with HIV/AIDS (PLWH/A)

Under normal circumstances, these two numbers are equal and estimates of the # of persons living in an area at mid-year (as provided by the US Census) are equal to the actual number of person years at risk because any change over the course of the year is assumed to be consistent and therefore the midpoint reflects an average. However, in cases where the change in population is sudden this assumption is no longer valid and the mid-year no longer represents a true reflection of person years. It should be expected therefore that the mid-year estimate for New Orleans might violate this assumption since the entire city was under a mandatory evacuation order and only allowed a staggered and slow return to their homes.

Under large scale evacuation events, or other instances where the census does not accurately reflect the number of person years, disease rates will be drastically *underestimated*. Van Landingham [34,35] explained this phenomenon as applied to New Orleans murder rate. Due to the large population loss in the last months of 2005, there was an apparent drop in murders that was actually an artifact of calculations. After applying a corrected population estimate of the average person years at risk as a denominator the actual rate was similar to previous years.

Rates of HIV/AIDS calculated as those murder rates can produce incorrect estimates. In 2008 Robinson et al. [36] applied corrected estimates of the person years at risk to New Orleans HIV/ AIDS diagnosis data and found that there was a dramatic spike in disease diagnosis rates in the year following Katrina (Figure 7).

### **4. Structural and policy level**

secondary active surveillance or reporting systems, such as laboratory reports, to document the return or relocation of persons living with HIV/AIDS. Cases with any available information were used as a sample of all cases to impute return of all persons living with HIV/AIDS. Both of these methods were recomputed at regular intervals in order to inform and direct the state

Figure 6 shows the estimated return of *persons living with HIV/AIDS* (PLWH/A) to Orleans Parish at time intervals consistent with the available population data. The rate of return of persons living with HIV/AIDS, using the secondary surveillance estimates, consistently matches the return of the general population. Not shown here, however, are the group specific estimates, which again point to disproportionate return based on race, sex, and geographic area of residence. Surprisingly, mode of transmission was an important factor in the ability to return, with men who have sex with men returning much earlier and at higher rates. However, this may be confounded with the geographic and socioeconomic characteristics of where many

Disease rate is often calculated as the number of disease cases divided by the number of persons in the population. However, more formal definitions would introduce time and replace the number of persons in the population with number of person-years at risk in the population.

**Estimated Number of PLWH/A in Orleans Parish**

August March August February October May Nov **2006 2007 2008**

**Figure 6.** Estimated numbers of returning persons living with HIV/AIDS (PLWH/A)

surveillance population

**% Return**

and local health department during this critical period.

62 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

of them resided.

**3.3. Incidence**

0

1000

2000

3000

**#PLWH/A**

4000

5000

We have already discussed how some persons living with HIV/AIDS and persons at risk may have had some difficulty in accessing needed programs services such as family planning or reproductive health needs [15] one year following the storm. Many of these interventions may have been traditionally sought at local public clinics, many of which remained closed until well after this time. This would be an example of one structural factor that could influence the epidemic as a result of Katrina. Thus, Katrina influenced the epidemic at a policy or structural level to the extent that clinic closures acted as a barrier to utilization of family planning services or other reproductive health needs that could've been used in the prevention of HIV or unplanned pregnancies.

Clearly one major concern following a natural disaster is in maintaining the infrastructure of the health care system. For those impacted with a disease such as HIV this is of vital importance and disruptions to the system can mean fluctuations in the delivery and availability of badly needed drugs or access to drug supplemental assistance programs. Several reports document‐ ed the recovery of the health care system and health care providers such as the Medical Center of Louisiana of New Orleans. One year after Katrina over 50% of professionals surveyed from the American College of Emergency Providers reported very little or no progress in the emergency care system [37]. Though improvements were marked and continue to improve to this day, a great deal of uncertainty existed well past that time [18] including the question of the future of the State's safety net health care system for indigent care.

plans should be developed by staff that is tailored towards their client base and explains the need for and how to access services such as getting assistance with medication needs during these crises. Finally, providers should be aware that different funding mechanisms may be impacted by these events differentially and anticipate the results of that potential fiscal

Impact of Hurricane Katrina on the Louisiana HIV/AIDS epidemic: A Socio-Ecological Perspective

http://dx.doi.org/10.5772/55472

65

Other policy level factors could easily influence disease or the way in which Katrina impacts the epidemic. For example, how recovery money is allocated to rebuild neighborhoods, or to rebuild the health care system itself. Staffing and health department decisions or capacity to

This chapter presents a summary of the numerous impacts that were observed after Hurricane Katrina on the population and individuals who are living with or at risk for HIV/AIDS in Louisiana and the New Orleans area. These findings are furthermore interpreted in accordance with the Socio-Ecological Model of Health in order to conceptualize how a major disaster like Katrina can have long reaching impacts on not just the individual but on entire communities and systems under which people live. It is hoped that this model will allow future researchers to more fully understand the impact of disasters in a new light, as well as provide valuable insight into the experience of public health professionals working in disaster recovery

1 Louisiana State University Health Sciences Center in New Orleans, School of Public Health

2 Louisiana Office of Public Health – STD/HIV Program Office, Poydras, New Orleans, LA,

[1] Blake E.S., Landsea C.W., Gibney E.J. The deadliest, costliest and most intense United States tropical cyclones from 1851 to 2010 (and other frequently requested facts).


2011; NOAA Technical Memorandum NWS NHC-6.

disruption.

**5. Summary**

conditions.

USA

**References**

**Author details**

William T. Robinson1,2\*

Address all correspondence to: Billy.robinson@la.gov

compete for funding also may be important.

**Figure 7.** Corrected HIV rates in Orleans Parish 2004- 2007

Concerted efforts did take place to ensure quality care of persons living with HIV/AIDS. One example of that is the recovery of the Medical Center of Louisiana *HIV Outpatient* (HOP) clinic [38-41]. Approximately one month after the storm the New Orleans Mayor reopened areas of the city by selected ZIP codes. Immediately following this, HOP physicians and staff had established the means to provide medication and prescriptions to persons living with HIV/ AIDS prior to reopening of their office space. Because of the importance of maintaining adherence, staff and social workers went so far as to advertise this service in local bars. By Summer 2006 staff had occupied a temporary space and restored many services, with some exceptions including laboratory testing.

Extensive efforts by state Office of Public Health personnel and social workers also resulted in minimal disruption to the state Ryan White Title II funded *AIDS Drug Assistance Program* (ADAP). These efforts included agreements with other states in order to preserve services for those persons living with HIV/AIDS who were dislocated to other states [42]. This strategy was successful in that the results of a collaborative needs assessment New Orleans persons living with HIV/AIDS who utilized services such as ADAP, relatively few (15%) reported not being able to access these services in the six months following Katrina [43]. Also, while there was a reduction in the number of statewide unduplicated ADAP clients in the quarter following Katrina, that number of quarterly clients remained stable in the three years following the storm, potentially reflecting the fact that a number of clients may have not returned.

Clark et al. [41] made several recommendations for increasing emergency preparedness capacity. Physicians and other health workers should reinforce patient responsibility in knowing about their health indicators and their own medication need. Systems should move towards electronic health records and plan for storage and backup of needed data. Disaster plans should be developed by staff that is tailored towards their client base and explains the need for and how to access services such as getting assistance with medication needs during these crises. Finally, providers should be aware that different funding mechanisms may be impacted by these events differentially and anticipate the results of that potential fiscal disruption.

Other policy level factors could easily influence disease or the way in which Katrina impacts the epidemic. For example, how recovery money is allocated to rebuild neighborhoods, or to rebuild the health care system itself. Staffing and health department decisions or capacity to compete for funding also may be important.

#### **5. Summary**

Concerted efforts did take place to ensure quality care of persons living with HIV/AIDS. One example of that is the recovery of the Medical Center of Louisiana *HIV Outpatient* (HOP) clinic [38-41]. Approximately one month after the storm the New Orleans Mayor reopened areas of the city by selected ZIP codes. Immediately following this, HOP physicians and staff had established the means to provide medication and prescriptions to persons living with HIV/ AIDS prior to reopening of their office space. Because of the importance of maintaining adherence, staff and social workers went so far as to advertise this service in local bars. By Summer 2006 staff had occupied a temporary space and restored many services, with some

2004 2005 2006 2007

Cases Rate per 100,000

**Rate per 100,000**

**New HIV Diagnoses Orleans Parish**

Extensive efforts by state Office of Public Health personnel and social workers also resulted in minimal disruption to the state Ryan White Title II funded *AIDS Drug Assistance Program* (ADAP). These efforts included agreements with other states in order to preserve services for those persons living with HIV/AIDS who were dislocated to other states [42]. This strategy was successful in that the results of a collaborative needs assessment New Orleans persons living with HIV/AIDS who utilized services such as ADAP, relatively few (15%) reported not being able to access these services in the six months following Katrina [43]. Also, while there was a reduction in the number of statewide unduplicated ADAP clients in the quarter following Katrina, that number of quarterly clients remained stable in the three years following the storm, potentially reflecting the fact that a number of clients may have not returned.

Clark et al. [41] made several recommendations for increasing emergency preparedness capacity. Physicians and other health workers should reinforce patient responsibility in knowing about their health indicators and their own medication need. Systems should move towards electronic health records and plan for storage and backup of needed data. Disaster

exceptions including laboratory testing.

64 Natural Disasters - Multifaceted Aspects in Management and Impact Assessment

**Cases**

**Figure 7.** Corrected HIV rates in Orleans Parish 2004- 2007

This chapter presents a summary of the numerous impacts that were observed after Hurricane Katrina on the population and individuals who are living with or at risk for HIV/AIDS in Louisiana and the New Orleans area. These findings are furthermore interpreted in accordance with the Socio-Ecological Model of Health in order to conceptualize how a major disaster like Katrina can have long reaching impacts on not just the individual but on entire communities and systems under which people live. It is hoped that this model will allow future researchers to more fully understand the impact of disasters in a new light, as well as provide valuable insight into the experience of public health professionals working in disaster recovery conditions.

### **Author details**

William T. Robinson1,2\*

Address all correspondence to: Billy.robinson@la.gov

1 Louisiana State University Health Sciences Center in New Orleans, School of Public Health - Behavioral and Community Health Sciences, Gravier; New Orleans LA, USA

2 Louisiana Office of Public Health – STD/HIV Program Office, Poydras, New Orleans, LA, USA

#### **References**

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[2] Louisiana Department of Health and Hospitals,. Reports of Missing and deceased. 2006.

[15] Kissinger P, Schmidt N, Sanders C, Liddon N. The effect of the hurricane Katrina dis‐ aster on sexual behavior and access to reproductive care for young women in New Orleans. Sexually Transmitted Diseases 2007;34(11):883-6 %U http://

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http://dx.doi.org/10.5772/55472

67

[16] Kissinger P, Liddon N, Schmidt N, Curtin E, Salinas O, Narvaez A. HIV/STI Risk be‐ haviors among Latino migrant workers in New Orleans post-Hurricane Katrina dis‐

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[19] Kishore V, Theall KP, Robinson W, Pichon J, Scribner R, Roberson E, et al. Resource loss, coping, alcohol use, and posttraumatic stress symptoms among survivors of Hurricane Katrina: a cross-sectional study. Am J Disaster Med 2008 Nov-Dec;3(6):

[20] Wagner KD, Pollini RA, Patterson TL, Lozada R, Ojeda VD, Brouwer KC, et al. Cross-border drug injection relationships among injection drug users in Tijuana,

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[2] Louisiana Department of Health and Hospitals,. Reports of Missing and deceased.

[3] Crouse-Quinn S. Hurricane Katrina: A social and public health disaster. American

[4] Kaiser Family F. Assessing the number of people with HIV/AIDS in areas affected by

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[10] Farley TA. Sexually transmitted diseases in the Southeastern United States: location,

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uation and risk behavior patterns pst hurricane Katrina. 2006.

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health 2008 Apr;98(4):666-8.


[27] Montaner J, Hogg R. Implications of the Henan Province report on the treatment as prevention debate. J Acquir Immune Defic Syndr 2011 Mar;56(3):e101; author reply e101-2.

[41] Clark RA, Mirabelli R, Shafe J, Broyles S, Besch L, Kissinger P. The New Orleans HIV outpatient program patient experience with Hurricane Katrina. J La State Med Soc

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[42] Louisiana Office of Public Health STD/HIV AIDS Program. personal communication.

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[41] Clark RA, Mirabelli R, Shafe J, Broyles S, Besch L, Kissinger P. The New Orleans HIV outpatient program patient experience with Hurricane Katrina. J La State Med Soc 2007 Sep-Oct;159(5):276, 278-9, 281.

[27] Montaner J, Hogg R. Implications of the Henan Province report on the treatment as prevention debate. J Acquir Immune Defic Syndr 2011 Mar;56(3):e101; author reply

[28] RIA Novisti. Japan's PM urges people to clear 20-km zone around Fukushima NPP. Available at: http://en.rian.ru/world/20110315/163008635.html. Accessed 12/10, 2012.

[29] Stone G, Grant T, Weaver N. Rapid population estimate project January 28-29 survey

[30] Louisiana Public Health Institute. 2006 Louisiana Health and Population Survey.

[31] Claritas. Hurricane Katrina-adjusted population estimates. Available at: http:// www.claritas.com/claritas/Default.jsp?ci=1&pn=hurricane\_katrina\_data., 2007. [32] Feasibility of neighborhood surveys in post-Katrina New Orleans: Development of a systematic social observation tool. New Orleans, LA: Proceedings of the American

[33] Brumsfa DL, Overfelt D, Picou S, Bankston CL editors. The sociology of Katrina: Per‐ spectives on a modern catastrophe. : Rowman & Littlefield Publishers; 2007.

[34] Van Landingham MJ. Murder rates in New Orleans, La, 2004-2006. American journal

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[37] After the Storm - Health Care Infrastructure in Post-Katrina New Orleans %U http://

[38] Clark RA, Besch L, Murphy M, Vick J, Gurd C, Broyles S, et al. Six months later: The effect of Hurricane Katrina on health care for persons living with HIV/AIDS in New

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Statistical Association - Hard to Reach Populations; 2012.

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e101-2.

report. 2006.


**Chapter 4**

**Impacts of Cyclone Nargis on**

Peter H. Calkins and Ngu Wah Win

http://dx.doi.org/10.5772/54140

**1. Introduction**

fected by humans.

**1.2. Case study of cyclone Nargis**

**Social Capital and Happiness in**

Additional information is available at the end of the chapter

**Slightly and Heavily Affected Areas of Myanmar**

Over the past twelve years, natural disasters such as floods, tornadoes, cyclones, volcanic eruptions, earthquakes, *tsunamis* and landslides have intensified, violently terminating thou‐ sands of lives and leading to vast financial and environmental losses. The magnitude of those losses depends upon the vulnerability of the affected population, which is in turn in‐ fluenced by the country's poverty level, social safety nets, inter-group inequalities, educa‐ tional system, infrastructure, other institutions or policies, and prevention technologies. Still, many types of disasters brush mockingly aside even the most ingenious technologies per‐

Multiple causes explain the increased frequency and destructiveness of such events. Human abuse of the earth's resources, unrestrained pollution, and the release of greenhouse gases may not actually cause natural disasters, but they have clearly made them worse and more frequent [1]. Destruction could be eased through short-term early warning systems and long-term poverty reduction programs. Human cultures, beliefs, social interactions and hap‐ piness are deeply affected by disasters and constitute a set of human resources that can be used to rebuild society afterwards. Only if the physical and emotional impacts, as well as the successful and unsuccessful prevention and mitigation strategies, of such disasters are

One of the least analyzed natural disasters of the past 20 years is the cyclone -- decep‐ tively named *daffodil* (Nargis) – that assailed the *Subjective Well-Being (SWB)* of small

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© 2013 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution,

better understood may societies hope to reduce suffering from future disasters.
