**2.2 Bridge Health Monitoring System**

446 Fuzzy Inference System – Theory and Applications

impact on the diagnosis and decisions to be made. For example a defect reported from one inspector may be different from the others. In order to overcome this unwilling fact nondestructive testing methods have been suggested for objective inspection in recent decades. Although these methods are more accurate than visual inspection, there are some kinds of problems. Interpretation of their results needs experience together with the knowledge about deteriorations of bridge material and damage types of its elements. Therefore results of visual inspection and some nondestructive testing methods are inherently uncertain and vague. It is to notify that the linguistically describing results are more uncertain and vague. Degree of uncertainty and level of vagueness depend on many parameters such as inspector's experience, definition of symptom or deterioration type, level

Among many methods it seems that models based on artificial intelligence which apply soft computing methods are more attractive for dealing with uncertain and vague data in managing bridges. Fuzzy Inference System (FIS) is capable of being used in areas for decision making when data is uncertain. One of the most attractive advantages of FIS is its

Based on the type of problems with uncertain and vague data, different FIS modeling types can be regarded as appropriate and easy methods for managing bridges (Tarighat &

After introduction section this chapter continues with section 2 and its subsections showing the need for managing bridges and introduces Bridge Management System and Bridge Health Monitoring system. Ambiguity in diagnosis and decision making based on the collected data in above mentioned systems are discussed in section 3. Section 4 explains why fuzzy inference systems are suitable in managing systems. Its subsections are about fuzzy inference systems including Mamdani's method and Adaptive Neuro Fuzzy Inference System (ANFIS) method. Then some case studies and some typical applications of fuzzy inference system for managing

All the inspection data should be stored in inventory and inspection databases and they should be used to get information for what to do in the next step of managing bridges. Therefore a kind of managing system is required to use the data and making appropriate

Bridge Management System (BMS) has great roles in managing bridges. The main feed into any BMS is inspection data. It is designed to provide information not easily available from

Improvements in the type and quality of data that is collected, stored, managed, and

of defect categorizations and many others.

tolerability to noisy (uncertain and vague) data.

bridges are included in section 5. Section 6 concludes this chapter.

Miyamoto, 2009).

**2. Managing bridges** 

and logical decisions for further actions.

available data. BMS can provide the following:

Realistic and reliable forecasts of future needs

A logical method for setting priorities for current needs

Ways to implement changes in management philosophies and goals

used in a bridge system analysis

**2.1 Bridge Management System** 

Generally in advanced BMS there is a module named Bridge Health Monitoring System (BHMS). BHMS can be considered as one of the most important parts of a practical BMS.

In bridge structures there are many different unforeseen conditions that we do not have enough information about them. Although in design codes we are forced to consider some parameters or factors affecting structural behavior there are even more items that we cannot consider them practically. Therefore it is probable to have some risk for not fulfilling the complete standards of safety. Presently health and performance are described based on subjective indices which are not precise. In addition there are some possibilities for unobserved and undiscovered symptoms, deteriorations, and damages in bridge structures due to limited or no accessibility to some elements. The immediate consequence is that the real health index is not that thought and considered. This unwilling fact impacts the effectiveness and reliability of any managerial decision irrespective of sophistication in the management process. Moreover, even experienced engineers may find visual signs of defects, deterioration and damage and cannot be able to diagnose the causative mechanisms, or their impact on the reliability of the bridge and its global health. The global health of a bridge as a whole system, inclusive of the performance criteria corresponding to each limit states is actually what is needed for effective managerial decisions. There are needs of periodic inspections to detect deterioration resulting from normal operation and environmental attack or inspections following extreme events, such as earthquakes or hurricanes. To quantify these system performance measures requires some means to monitor and evaluate the integrity of bridge structures while in service (Wang & Zong, 2002). BHMS can help managers to know about the healthiness of a bridge at any given time.

BHMS may also have other applications. For example any damage in some elements of a bridge has direct effect on its load bearing capacity especially vibration characteristics. In other words this effect can change the overall behavior of the bridge under loads which cause the bridge to vibrate. Based on this fact any method for damage detection which is

Fuzzy Inference System as a Tool for Management of Concrete Bridges 449

Data Acquisition

Modeling Identification Damage Location

Recently it is become obvious that although BMS and BHMS can be precisely used for managerial issues but there is an important fact about the collected data. Data is not perfect. It is found that the results and data-driven interpretations are prone to some degree of vagueness. Therefore, the obtained data that should be altered to information has inherently

In managing a bridge we are concerned about condition state or standard level of a requirement. For example we want to know what the current condition state of a bridge is and to how it has been deviated from the previous and known condition state (Washington

some degrees of uncertainty and vagueness (Tarighat & Miyamoto, 2009).

Updating *Diagnosis*

Simulation or Computation

Technique *Monitoring*

Signal Processing

and Severity

Repair/Maintenance Strategies

> *Condition Assessment*

Remote sensing and wireless sensor networks

Structural System and Material

Excitation Sensor/Actuator, NDE

Modal

Estimation of Remaining Strength

Prediction of Service Life

Reliability Analysis and Evaluation

Analysis of Life-Cycle Cost

Fig. 1. Basic components of a typical BMS and BHMS

State Bridge Inspection Manual, 2010).

**3. Ambiguity in diagnosis and decision making** 

Life cycle performance design

capable of showing the location and severity of damage can be considered useful for bridge maintenance and repair departments (Haritos, 2000).

Bridge real time monitoring during service provides information on structural behavior under predicted loads, and also registers the effects of unpredicted overloading. Data obtained by monitoring is useful for damage detection, safety evaluation, and determination of the residual load bearing capacity of bridges. Early damage detection is particularly important because it leads to appropriate and timely interventions. If the damage is not detected, it continues to propagate and the bridge no longer guarantees required performance levels. Late detection of damage results in either very elevated refurbishment costs or, in some cases, the bridge has to be closed and dismantled. In seismic areas the importance of monitoring is more critical. Subsequent auscultation of a bridge structure that has not been monitored during its construction can serve as a basis for prediction of its present and future structural behavior. Based on these facts there are many applications for developing BHMS. As mentioned one of the most important applications of BHMS is damage detection. Among the attractive methods for damage detection problems are models based on artificial intelligence especially soft computing methods (Ou et al., 2006; Wu & Abe, 2003).

As it is used several times in above paragraphs, health can be defined as the reliability of a bridge structure to perform adequately for the required functionalities (Aktan et al., 2002). Some of these functionalities are:


It is not possible to quantify health and reliability of a bridge system for many of the limit states without extensive data that we often do not have. Based on a general definition monitoring is the frequent or continuous observation or measurement of structural conditions or actions (Wenzel & Tanaka, 2006). There is another definition which gives more detail: structural health monitoring is the use of in-situ, non-destructive sensing and analysis of structural characteristics, including the structural response, for detecting changes that may indicate damage or degradation [Housner & Bergman, 1997). Fig. 1 shows basic components of a typical BMS and BHMS.

In summary BMS and BHMS are used to:


capable of showing the location and severity of damage can be considered useful for bridge

Bridge real time monitoring during service provides information on structural behavior under predicted loads, and also registers the effects of unpredicted overloading. Data obtained by monitoring is useful for damage detection, safety evaluation, and determination of the residual load bearing capacity of bridges. Early damage detection is particularly important because it leads to appropriate and timely interventions. If the damage is not detected, it continues to propagate and the bridge no longer guarantees required performance levels. Late detection of damage results in either very elevated refurbishment costs or, in some cases, the bridge has to be closed and dismantled. In seismic areas the importance of monitoring is more critical. Subsequent auscultation of a bridge structure that has not been monitored during its construction can serve as a basis for prediction of its present and future structural behavior. Based on these facts there are many applications for developing BHMS. As mentioned one of the most important applications of BHMS is damage detection. Among the attractive methods for damage detection problems are models based on artificial intelligence especially soft computing methods (Ou et al., 2006;

As it is used several times in above paragraphs, health can be defined as the reliability of a bridge structure to perform adequately for the required functionalities (Aktan et al., 2002).

It is not possible to quantify health and reliability of a bridge system for many of the limit states without extensive data that we often do not have. Based on a general definition monitoring is the frequent or continuous observation or measurement of structural conditions or actions (Wenzel & Tanaka, 2006). There is another definition which gives more detail: structural health monitoring is the use of in-situ, non-destructive sensing and analysis of structural characteristics, including the structural response, for detecting changes that may indicate damage or degradation [Housner & Bergman, 1997). Fig. 1 shows basic

maintenance and repair departments (Haritos, 2000).

Wu & Abe, 2003).

Utility

Some of these functionalities are:

Serviceability and durability

Safety at conditional limit states

components of a typical BMS and BHMS. In summary BMS and BHMS are used to:

 Predicting the remaining service life Structural/system identification

Forced vibration-based damage detection

 Structural management Increase of safety

Knowledge improvement

Safety and stability of failure at ultimate limit states

Rating the current condition of bridge or its components

Damage detection/diagnosis and damage localization

 Wind induced vibration-based damage detection Ambient vibration-based damage detection

Fig. 1. Basic components of a typical BMS and BHMS
