Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

Chung C. Fu, Yifan Zhu and Kuang-Yuan Hou

## Abstract

Infrastructures, including bridges, tunnels, sewers, and telecommunications, may be exposed to environmental-induced or traffic-induced deformation and vibrations. Some infrastructures, such as bridges and roadside upright structures, may be sensitive to vibration and displacement where several different types of dynamic and static sensors may be used for their measurement of sensitivity to environmental-induced loads, like wind and earthquake, and traffic-induced loads, such as passing trucks. Remote sensing involves either in situ, on-site, or airborne sensing where in situ sensors, such as strain gauges, displacement transducers, velometers, and accelerometers, are considered conventional but more durable and reliable. With data collected by accelerometers, time histories may be obtained, transformed, and then analyzed to determine their modal frequencies and shapes, while with displacement and strain transducers, structural deflections and internal stress distribution may be measured, respectively. Field tests can be used to characterize the dynamic and static properties of the infrastructures and may be further used to show their changes due to damage. Additionally, representative field applications on bridge dynamic testing, seismology, and earthborn/construction vibration are explained. Sensor data can be analyzed to establish the trend and ensure optimal structural health. At the end, five case studies on bridges and industry facilities are demonstrated in this chapter.

Keywords: health monitoring, accelerometers, velometers, displacement transducers, strain sensors, frequency response function, cross-power spectrum, power spectral density, bridge dynamic testing, seismology, earthborn/construction vibration, infrastructure

## 1. Introduction

In order to acquire infrastructural health data, proper sensor knowledge and technology are required. This article first introduces in situ remote sensing and then provides a review of some sensors that are useful and currently implemented in health monitoring projects, especially those associated with vibration.

A project on the development of a self-sustained wireless integrated structural health monitoring (ISHM) system for highway bridges was sponsored by the USDOT Research and Innovative Technology Administration (RITA) [1]. Figure 1 shows the wireless ISHM system with remote sensing ability: (1) wireless sensor

nodes including AE sensors, strain gages, accelerometers, thermocouples, etc.; (2) wireless smart sensor network with (3) energy harvester; (4) data acquisition system (DAQ ), with wireless communication modem; and (5) web-based remote data processing and data storage for application.

transducers, we may obtain acceleration through differentiation or double differentiation, respectively. Usually, unless there are special circumstances, the

suggested method to measure vibration is with an accelerometer. However, care is required to remove accelerations of very low frequencies for possible noises if any

Accelerometers and strain sensors are widely used dynamic and static monitoring sensors. The modern-day systems are small, lightweight, and robust and are typically quite simple to calibrate and to convert output to acceleration or strain data. Accelerometers are useful for measuring with low to very high sampling rates. They have shown to be useful in a wide variety of applications. On the other hand, velocity sensors are generally used to measure dynamic response in the low- to medium-range frequencies. They are typically used for similar applications as

For the static monitoring sensor, displacement transducers are used to measure relative displacement. These sensors are available in both contacting devices, like string pot and linear variable differential transformer (LVDT), and non-contacting devices, like laser displacement, global positioning systems (GPS), and photogrammetry. The major limitation for contacting displacement-measuring devices in the field is that the measured displacement is a relative displacement. GPS-type sensors are gradually more often used in civil engineering studies because of recent developments allowing measurements to be taken at high fidelity. Displacement measurements from laser sensors, ultrasonic distance sensors, and strain pot were used on different occasions to determine the vertical deflection of a bridge. These techniques are useful because they can result in relative and absolute displacement states. Strain sensors, including optical fiber strain, can be monitored at dynamic rates, while traditional foil strain gauges have been widely used on civil engineering

2. Mathematical models for computing accelerometer sensor data

The data acquisition system may be set to measure acceleration time histories and calculate frequency response function (FRF), cross-power spectrum (CPS),

For a continuous time series, x tð Þ, defined on the interval from 0 to T, the

ð T

x tð Þe

This function is complex, and the magnitude is typically plotted in engineering

where \* denotes a complex conjugate. The power spectrum is a real-valued

The power spectral density (auto-spectral density, or abbreviated as PSD),

�i2<sup>π</sup>ftdt (1)

j j X f ð Þ <sup>2</sup> <sup>¼</sup> <sup>X</sup>ð Þ<sup>f</sup> <sup>X</sup>ð Þ<sup>f</sup> (2)

.

0

Fourier spectrum (Fourier transform), X f ð Þ, is defined in Eq. (1) as

Xð Þ¼ f

<sup>2</sup> or g's, versus frequency.

�<sup>1</sup> <sup>p</sup> and <sup>f</sup> <sup>¼</sup> cyclic frequency Hz ð Þ.

This power spectrum is defined in Eq. (2) as

frequency domain function and has the units of ð Þ EU <sup>2</sup>

integration to velocity or displacement is needed.

DOI: http://dx.doi.org/10.5772/intechopen.83500

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

accelerometers [2].

structures, even in remote sensing.

and power spectral density (PSD) [3].

where <sup>i</sup> <sup>¼</sup> ffiffiffiffiffiffi

units (EU), such as m=s

GXXð Þf , is defined in Eq. (3) as

109

In situ sensors may include the capability to collect static and dynamic data and then apply algorithm to extract and combine relevant condition information from sensor data. Typical vibrational sensors used include accelerometers and velometers (velocity transducers), while static sensors include displacement transducers, strain gauges (transducers), tilt meters, and weather-related sensors to measure and record temperature, humidity, barometric pressure, wind velocity, wind direction, etc. When using vibration data, especially in conjunction with modeling systems, the data is often measured in the form of acceleration, velocity, and displacement. Sometimes different analyses require measured signals in different forms. Even if we measure in the form of acceleration, velocity, or displacement (Figure 2), we may apply simple mathematics to convert between them through integration or differentiation. For instance, if the measured signal is from accelerometers, we may obtain the velocity through integration and displacement through double integration. On the other hand, if the measured signal is from velocity or displacement

Figure 1. Remote wireless bridge monitoring system.

#### Figure 2.

Measured signals in different forms: (a) acceleration (raw data), (b) velocity (single integration from acceleration), and (c) displacement (double integration from acceleration).

#### Utilization of Dynamic and Static Sensors for Monitoring Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.83500

transducers, we may obtain acceleration through differentiation or double differentiation, respectively. Usually, unless there are special circumstances, the suggested method to measure vibration is with an accelerometer. However, care is required to remove accelerations of very low frequencies for possible noises if any integration to velocity or displacement is needed.

Accelerometers and strain sensors are widely used dynamic and static monitoring sensors. The modern-day systems are small, lightweight, and robust and are typically quite simple to calibrate and to convert output to acceleration or strain data. Accelerometers are useful for measuring with low to very high sampling rates. They have shown to be useful in a wide variety of applications. On the other hand, velocity sensors are generally used to measure dynamic response in the low- to medium-range frequencies. They are typically used for similar applications as accelerometers [2].

For the static monitoring sensor, displacement transducers are used to measure relative displacement. These sensors are available in both contacting devices, like string pot and linear variable differential transformer (LVDT), and non-contacting devices, like laser displacement, global positioning systems (GPS), and photogrammetry. The major limitation for contacting displacement-measuring devices in the field is that the measured displacement is a relative displacement. GPS-type sensors are gradually more often used in civil engineering studies because of recent developments allowing measurements to be taken at high fidelity. Displacement measurements from laser sensors, ultrasonic distance sensors, and strain pot were used on different occasions to determine the vertical deflection of a bridge. These techniques are useful because they can result in relative and absolute displacement states. Strain sensors, including optical fiber strain, can be monitored at dynamic rates, while traditional foil strain gauges have been widely used on civil engineering structures, even in remote sensing.

## 2. Mathematical models for computing accelerometer sensor data

The data acquisition system may be set to measure acceleration time histories and calculate frequency response function (FRF), cross-power spectrum (CPS), and power spectral density (PSD) [3].

For a continuous time series, x tð Þ, defined on the interval from 0 to T, the Fourier spectrum (Fourier transform), X f ð Þ, is defined in Eq. (1) as

$$X(f) = \int\_0^T \varkappa(t)e^{-i2\pi ft}dt\tag{1}$$

where <sup>i</sup> <sup>¼</sup> ffiffiffiffiffiffi �<sup>1</sup> <sup>p</sup> and <sup>f</sup> <sup>¼</sup> cyclic frequency Hz ð Þ.

This function is complex, and the magnitude is typically plotted in engineering units (EU), such as m=s <sup>2</sup> or g's, versus frequency.

This power spectrum is defined in Eq. (2) as

$$|\mathbf{X}(f)|^2 = \mathbf{X}(f)\mathbf{X}(f) \tag{2}$$

where \* denotes a complex conjugate. The power spectrum is a real-valued frequency domain function and has the units of ð Þ EU <sup>2</sup> .

The power spectral density (auto-spectral density, or abbreviated as PSD), GXXð Þf , is defined in Eq. (3) as

nodes including AE sensors, strain gages, accelerometers, thermocouples, etc.; (2) wireless smart sensor network with (3) energy harvester; (4) data acquisition system (DAQ ), with wireless communication modem; and (5) web-based remote

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications,Tsunami…

Measured signals in different forms: (a) acceleration (raw data), (b) velocity (single integration from

acceleration), and (c) displacement (double integration from acceleration).

In situ sensors may include the capability to collect static and dynamic data and then apply algorithm to extract and combine relevant condition information from sensor data. Typical vibrational sensors used include accelerometers and velometers (velocity transducers), while static sensors include displacement transducers, strain gauges (transducers), tilt meters, and weather-related sensors to measure and record temperature, humidity, barometric pressure, wind velocity, wind direction, etc. When using vibration data, especially in conjunction with modeling systems, the data is often measured in the form of acceleration, velocity, and displacement. Sometimes different analyses require measured signals in different forms. Even if we measure in the form of acceleration, velocity, or displacement (Figure 2), we may apply simple mathematics to convert between them through integration or differentiation. For instance, if the measured signal is from accelerometers, we may obtain the velocity through integration and displacement through double integration. On the other hand, if the measured signal is from velocity or displacement

data processing and data storage for application.

Figure 1.

Figure 2.

108

Remote wireless bridge monitoring system.

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications,Tsunami…

$$\mathbf{G}\_{\text{XX}}(f) = \frac{2}{T} \mathbf{E}\left[ \left( \mathbf{X}(f) \right)^2 \right] \tag{3}$$

where E n½ � indicates an ensemble average for a specific t over n samples of Xð Þf . This PSD is a real-valued frequency domain function and has the units of EU<sup>2</sup> =Hz.

The cross-power spectrum (cross-spectrum density, or abbreviated as CPS), GXYð Þf , relating two time histories, x tð Þ and y tð Þ, is defined in Eq. (3) as

$$G\_{XY}(f) = \frac{2}{T} E[X(f)Y(f)]\tag{4}$$

For a linear system, the frequency response function (transfer function, or abbreviated as FRF), Hð Þf , which relates an input Xð Þf to a response Yð Þf , is defined in Eq. (5) as

$$H(f) = \frac{\mathbf{Y}(f)}{\mathbf{X}(f)} = \frac{\mathbf{G}\_{\mathbf{XY}}(f)}{\mathbf{G}\_{\mathbf{XX}}(f)}\tag{5}$$

In actual dynamic testing, discrete time series are measured. Refer to Bendal and Piersol [4] for the discrete representations on the functions listed in Eqs. (1)–(5).

There are several factors that would affect system-level measurement accuracy, which are (1) sensitivity error and initial absolute offset, (2) nonlinearity of the data, (3) total offset variation from initial absolute offset, and (4) noise. To improve the accuracy, two- or three-point calibrations recommended by manufacturers may be needed.

The output spectrum (measured with accelerometers) can be assumed to be linearly related to the input spectrum through the FRF, which contains both resonant frequency and damping information of the vibrating system. Resonant frequencies can be determined from peaks in the output spectrum, and damping values can be determined by the half-power bandwidth (HPBW) method.

The damping ratio, or damping coefficient, <sup>ξ</sup>, is defined as <sup>c</sup>=cc <sup>¼</sup> <sup>c</sup>=<sup>2</sup> ffiffiffiffiffiffi km <sup>p</sup> to be used in the dynamic analysis. Normally, steel bridges have a low damping coefficient ξ≤0:02. The half-power (bandwidth) method is the most commonly used experimental method [5] to determine the damping in the structure by using two frequencies shown in Figure 3 and Eq. (6):

$$\xi = \frac{f\_2 - f\_1}{f\_2 + f\_1} \tag{6}$$

Mathematically the most common and easy way is to use the Rayleigh damping method with a linear combination of the mass and the stiffness matrices as Eq. (7):

$$
\mathcal{L} = a\_0 m - a\_1 k \tag{7}
$$

the two signals. One signal is termed the reference signal, and the process is repeated at various stations on the bridge to map out the mode shapes. Typically, in vibration testing FRFs are used to estimate the dynamic properties of a structure. Further interpreted from the CPS, it can be seen that two measured

Relationship between damping ratio and frequency for Rayleigh damping.

Figure 3.

Figure 4.

111

Half-power method to estimate damping by experiment.

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

DOI: http://dx.doi.org/10.5772/intechopen.83500

responses are correlated only at the resonant frequencies of the structure. Therefore, the CPS will show peaks corresponding to the resonant frequency which shows another method estimating the resonant frequencies from peaks in the

where c, m, and k are the damping, the mass, and the stiffness matrices, respectively, a<sup>0</sup> and a<sup>1</sup> are proportional constants, and cc represents the critical damping coefficient. The relationship between the damping ratio and the frequency for Rayleigh damping is shown in Figure 4. By simplification, these lead to Eq. (8):

$$\left\{ \begin{matrix} a\_0 \\ a\_1 \end{matrix} \right\} = \frac{2\xi}{\alpha\_n + \alpha\_m} \begin{Bmatrix} a\_n a\_m \\ 1 \end{Bmatrix} \tag{8}$$

The CPS plot between the signals from two accelerometers can then be used to determine the vibration mode shape information based on the relative phase of

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.83500

Figure 3. Half-power method to estimate damping by experiment.

Figure 4.

GXXð Þ¼ <sup>f</sup> <sup>2</sup>

GXYð Þf , relating two time histories, x tð Þ and y tð Þ, is defined in Eq. (3) as

GXYð Þ¼ <sup>f</sup> <sup>2</sup>

H f ð Þ¼ <sup>Y</sup>ð Þ<sup>f</sup>

defined in Eq. (5) as

be needed.

110

<sup>T</sup> E Xð Þ ð Þ<sup>f</sup> <sup>2</sup> h i

<sup>T</sup> E X½ � ð Þ<sup>f</sup> <sup>Y</sup>ð Þ<sup>f</sup> (4)

GXXð Þ<sup>f</sup> (5)

where E n½ � indicates an ensemble average for a specific t over n samples of Xð Þf .

The cross-power spectrum (cross-spectrum density, or abbreviated as CPS),

For a linear system, the frequency response function (transfer function, or abbreviated as FRF), Hð Þf , which relates an input Xð Þf to a response Yð Þf , is

<sup>X</sup>ð Þ<sup>f</sup> <sup>¼</sup> GXYð Þ<sup>f</sup>

In actual dynamic testing, discrete time series are measured. Refer to Bendal and Piersol [4] for the discrete representations on the functions listed in Eqs. (1)–(5). There are several factors that would affect system-level measurement accuracy, which are (1) sensitivity error and initial absolute offset, (2) nonlinearity of the data, (3) total offset variation from initial absolute offset, and (4) noise. To improve the accuracy, two- or three-point calibrations recommended by manufacturers may

The output spectrum (measured with accelerometers) can be assumed to be linearly related to the input spectrum through the FRF, which contains both resonant frequency and damping information of the vibrating system. Resonant frequencies can be determined from peaks in the output spectrum, and damping values can be determined by the half-power bandwidth (HPBW) method. The damping ratio, or damping coefficient, <sup>ξ</sup>, is defined as <sup>c</sup>=cc <sup>¼</sup> <sup>c</sup>=<sup>2</sup> ffiffiffiffiffiffi

used in the dynamic analysis. Normally, steel bridges have a low damping coefficient ξ≤0:02. The half-power (bandwidth) method is the most commonly used experimental method [5] to determine the damping in the structure by using two

> <sup>ξ</sup> <sup>¼</sup> <sup>f</sup> <sup>2</sup> � <sup>f</sup> <sup>1</sup> f <sup>2</sup> þ f <sup>1</sup>

Mathematically the most common and easy way is to use the Rayleigh damping method with a linear combination of the mass and the stiffness matrices as Eq. (7):

where c, m, and k are the damping, the mass, and the stiffness matrices, respectively, a<sup>0</sup> and a<sup>1</sup> are proportional constants, and cc represents the critical damping coefficient. The relationship between the damping ratio and the frequency for Rayleigh damping is shown in Figure 4. By simplification, these lead to Eq. (8):

The CPS plot between the signals from two accelerometers can then be used to determine the vibration mode shape information based on the relative phase of

ωnω<sup>m</sup> 1 � �

<sup>¼</sup> <sup>2</sup><sup>ξ</sup> ω<sup>n</sup> þ ω<sup>m</sup>

a0 a1 � � c ¼ a0m � a1k (7)

frequencies shown in Figure 3 and Eq. (6):

This PSD is a real-valued frequency domain function and has the units of EU<sup>2</sup>

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications,Tsunami…

(3)

=Hz.

km <sup>p</sup> to be

(6)

(8)

Relationship between damping ratio and frequency for Rayleigh damping.

the two signals. One signal is termed the reference signal, and the process is repeated at various stations on the bridge to map out the mode shapes. Typically, in vibration testing FRFs are used to estimate the dynamic properties of a structure. Further interpreted from the CPS, it can be seen that two measured responses are correlated only at the resonant frequencies of the structure. Therefore, the CPS will show peaks corresponding to the resonant frequency which shows another method estimating the resonant frequencies from peaks in the

response power spectra. Mode shapes are estimated from the relative magnitudes of these peaks, where relative phase information can be obtained from either the CPS or FRF and modal damping values can be obtained by applying the HPBW method to these peaks, which need very-high-frequency resolution to obtain the values. Mode shapes can be determined from cross-power spectra of the various accelerometer readings relative to the reference accelerometer [3]. Examples of field dynamic applications are shown in the next sections.

3.3 Earthborn/construction vibration

DOI: http://dx.doi.org/10.5772/intechopen.83500

structure by foundations or utilities.

vibrations are from less than 1.0 to 200 Hz.

will also be well below the 0.5 in/sec (15 mm/sec) criteria.

3.4 Types of accelerometers and their advantages/disadvantages

acceleration (in/sec<sup>2</sup> or mm/sec<sup>2</sup>

(19.2 mm/sec).

calculation.

113

Humans have varying sensitivities to vibrations at different frequencies. In general, humans are more sensitive to low-frequency vibration. Construction activities could induce vibrations that caused building surface movements, shaking or rattling of windows, hanging items, and lightweight furniture [7]. This type of lowfrequency vibrations, when acting on the structural component, can also produce an audible rumbling noise, which referred to earthborn noise. The noise could be a problem when the upper end of the range frequencies (60–200 Hz) dominates the originating vibration spectrum, or the construction activities are connected to the

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

Earthborn vibrations can be detected and measured by accelerometers which could be mounted to heavy blocks of steel (about 5–10 kg) directly placed directly on the ground or other surfaces by magnets [6, 8]. Activities and motions of the vibration-sensitive land shall be monitored and measured during constructions occur within 15 m (50 ft) to establish the level of vibrations. Construction projects of foundations, like pile driving, jackhammering, and soil compacting, may also produce high-level vibrations by their equipment operations. Measured vibration data from construction are commonly classified as broadband or random vibrations with various ranges of frequencies. The general frequency ranges of most earthborn

Vibration levels can be represented in terms of velocity (in/sec or mm/sec) or

The US Department of Transportation (USDOT) has guidelines for vibration levels from construction related to their activities and recommends that the maximum peak-particle-velocity levels remain below 0.05 in/sec (1.5 mm/sec) at the nearest structures. Vibration levels above 0.5 in/sec (1.5 mm/sec) have the potential to cause architectural damage to normal dwellings. The USDOT also states that vibration levels above 0.015 in/sec (0.45 mm/sec) are sometimes perceptible to people and the level at which vibration becomes annoying to people is 0.64 in/sec

Popular types of accelerometers used in the infrastructural areas are (1) bulk

The work principles of different types of accelerometers are based on piezoelectric effect due to accelerative forces and displacement sensing based on displacement of mass. The advantages of piezoelectric resistive are (1) rugged and inexpensive, (2) high impedance, (3) high sensitivity, and (4) high-frequency response. However, their disadvantages are (1) sensitive to temperature, (2) hys-

On the other hand, displacement sensing or seismic-type accelerometers are using spring-mass-damper system, and their advantages are (1) easy calculation, (2) simple and reliable, and (3) durable and efficient. Their disadvantages are (1) spring system not always accurate and (2) fluctuation in mass leading to wrong

micromachined capacitive, (2) bulk micromachined piezoelectric resistive, (3) capacitive spring-mass system based, and (4) laser accelerometers [9].

teresis error, (3) less longevity, and (4) decreased efficiency with time.

levels for construction activities are recognized as the highest during demolition activities and soil compacting. Vibration levels are required to remain below 0.5 in/sec (15 mm/sec) at residences along the project corridor and minimized risk for structural damage. Vibration levels from other general construction activities

), which demonstrates vibration severity. Vibration

## 3. Representative applications

## 3.1 Bridge dynamic testing

Dynamic testing on bridges has been conducted for many years. Measured data were usually in the form of deflections and strains, but some measurements were in acceleration. For bridge dynamic testing, ambient and forced vibrations can be performed.


By using accelerometers, acceleration time histories can be obtained, transformed into Fourier spectra and CPS, and then analyzed to determine damping, resonant frequencies, and corresponding modal shapes.

## 3.2 Seismology

Devices can be used to measure seismic data. Two types of sensors (transducers) were used by Caltrans to measure seismic record [6].


## 3.3 Earthborn/construction vibration

response power spectra. Mode shapes are estimated from the relative

spectra of the various accelerometer readings relative to the reference accelerometer [3]. Examples of field dynamic applications are shown in the

next sections.

performed.

loading.

3.2 Seismology

112

3. Representative applications

ratios, and mode shapes.

3.1 Bridge dynamic testing

magnitudes of these peaks, where relative phase information can be obtained from either the CPS or FRF and modal damping values can be obtained by applying the HPBW method to these peaks, which need very-high-frequency resolution to obtain the values. Mode shapes can be determined from cross-power

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications,Tsunami…

Dynamic testing on bridges has been conducted for many years. Measured data were usually in the form of deflections and strains, but some measurements were in acceleration. For bridge dynamic testing, ambient and forced vibrations can be

• Ambient vibration testing—Ambient vibrations in bridges can be induced by a wide variety of environmental factors, such as traffic, seismic, and wind

• Forced vibration testing—Some techniques of forced vibration testing of bridges such as variable frequency rotating dynamic shaker, servo-hydraulic inertial actuators, impact hammer, and controlled truck loading can be applied. Accelerometers can be used to determine the resonant frequencies, damping

By using accelerometers, acceleration time histories can be obtained, transformed into Fourier spectra and CPS, and then analyzed to determine

Devices can be used to measure seismic data. Two types of sensors (transducers)

• Accelerometer—An accelerometer measures acceleration directly by using the piezoelectric crystal material. This type of sensor, which is widely used by Caltrans, is pressure sensitive and can also obtain velocity and displacement with an integrator. Accelerometer is usually a small sensor with a wide frequency range, and typically not as sensitive as the seismometer. The frequency range could be narrowed from 0.1 to 1.0 KHz when using as large sensor as around 1 pound in weight and more sensitive technical methods,

• Seismometer—A seismometer, also called a velocity transducer, measures velocity directly using a signal conditioner. It measures low frequencies of ground motions (usually 1–200 Hz) and produces a voltage proportional to velocity through magnetic induction. A seismometer can catch low rate

damping, resonant frequencies, and corresponding modal shapes.

were used by Caltrans to measure seismic record [6].

vibrations during monitoring.

typically from 1.0 to several KHz.

Humans have varying sensitivities to vibrations at different frequencies. In general, humans are more sensitive to low-frequency vibration. Construction activities could induce vibrations that caused building surface movements, shaking or rattling of windows, hanging items, and lightweight furniture [7]. This type of lowfrequency vibrations, when acting on the structural component, can also produce an audible rumbling noise, which referred to earthborn noise. The noise could be a problem when the upper end of the range frequencies (60–200 Hz) dominates the originating vibration spectrum, or the construction activities are connected to the structure by foundations or utilities.

Earthborn vibrations can be detected and measured by accelerometers which could be mounted to heavy blocks of steel (about 5–10 kg) directly placed directly on the ground or other surfaces by magnets [6, 8]. Activities and motions of the vibration-sensitive land shall be monitored and measured during constructions occur within 15 m (50 ft) to establish the level of vibrations. Construction projects of foundations, like pile driving, jackhammering, and soil compacting, may also produce high-level vibrations by their equipment operations. Measured vibration data from construction are commonly classified as broadband or random vibrations with various ranges of frequencies. The general frequency ranges of most earthborn vibrations are from less than 1.0 to 200 Hz.

Vibration levels can be represented in terms of velocity (in/sec or mm/sec) or acceleration (in/sec<sup>2</sup> or mm/sec<sup>2</sup> ), which demonstrates vibration severity. Vibration levels for construction activities are recognized as the highest during demolition activities and soil compacting. Vibration levels are required to remain below 0.5 in/sec (15 mm/sec) at residences along the project corridor and minimized risk for structural damage. Vibration levels from other general construction activities will also be well below the 0.5 in/sec (15 mm/sec) criteria.

The US Department of Transportation (USDOT) has guidelines for vibration levels from construction related to their activities and recommends that the maximum peak-particle-velocity levels remain below 0.05 in/sec (1.5 mm/sec) at the nearest structures. Vibration levels above 0.5 in/sec (1.5 mm/sec) have the potential to cause architectural damage to normal dwellings. The USDOT also states that vibration levels above 0.015 in/sec (0.45 mm/sec) are sometimes perceptible to people and the level at which vibration becomes annoying to people is 0.64 in/sec (19.2 mm/sec).

#### 3.4 Types of accelerometers and their advantages/disadvantages

Popular types of accelerometers used in the infrastructural areas are (1) bulk micromachined capacitive, (2) bulk micromachined piezoelectric resistive, (3) capacitive spring-mass system based, and (4) laser accelerometers [9].

The work principles of different types of accelerometers are based on piezoelectric effect due to accelerative forces and displacement sensing based on displacement of mass. The advantages of piezoelectric resistive are (1) rugged and inexpensive, (2) high impedance, (3) high sensitivity, and (4) high-frequency response. However, their disadvantages are (1) sensitive to temperature, (2) hysteresis error, (3) less longevity, and (4) decreased efficiency with time.

On the other hand, displacement sensing or seismic-type accelerometers are using spring-mass-damper system, and their advantages are (1) easy calculation, (2) simple and reliable, and (3) durable and efficient. Their disadvantages are (1) spring system not always accurate and (2) fluctuation in mass leading to wrong calculation.

## 4. Case study 1: Wireless accelerometer sensing of a self-sustained wireless integrated structural health monitoring (ISHM) system on Beaufort #25 bridge, NC

A scalable integral structural health monitoring (ISHM) system sponsored by the USDOT had been developed by the University of Maryland (UMD) and North Carolina State University (NCSU) with the URS (later named AECOM) Corporation [1]. This system, with remote sensing capability, is designed to be suited for fatigue condition assessment of highway steel bridges. Furthermore, the ISHM system would help in damage detection and deterioration diagnosis in early stages, predicting the remaining service life more accurately when compared with the traditional SHM system with reliable technology to improve current inspection methods, and reduce the operating and maintenance costs.

The ISHM system based on wireless sensor networks entails a few recent innovations which applied the current state of the practice in remote sensing and highway infrastructure management. Accelerometers, in this system, are used for monitoring the vibration response of bridges so that the modal frequency information could be obtained and used to calibrate the finite element model of the monitored bridge.

boundary condition of the main span is changed between simply supported and cantilever due to the close or open of the main span. Thus, the researchers of NCSU chose the main span as the targeted monitoring case for the dynamic behavior

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

In this case, a row of smart sensors was attached to the bridge girders in the main span. The dynamic behavior was analyzed by data from accelerometers. Figure 6

considering the complex stress states.

The result of the field test of Beaufort #25 bridge.

DOI: http://dx.doi.org/10.5772/intechopen.83500

Figure 6.

Figure 7.

115

The first three mode shapes from FE analysis.

In this system, a new wireless piezoelectric sensor board had been designed and used. This board mainly consists of an 8-bit microcontroller, a FPGA, and a piezoelectric amplifier circuit. This device is enhanced with improved operating frequency and a four-wire, SPI-compatible interface while having lower power consumption. In the ISHM system, each single wireless sensor was tested on a shaker to verify that the developed sensor can recover the input information accurately. However, a single sensor could not catch enough data for structure monitoring and analysis. Thus, a number of wireless sensors along the bridge span are needed [1].

The example for the ISHM accelerometer monitoring case is the Structure No. 060025 Swing Bridge in Beaufort County, North Carolina (Figure 5, Beaufort #25 Bridge). The bridge consists of side spans and main spans. It should be noted that the structural support of the side span is a simply supported steel girder bridge, which has a relatively simple stress state compared with the main span because the

Figure 5. Sketch plan for monitoring system.

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.83500

Figure 6. The result of the field test of Beaufort #25 bridge.

4. Case study 1: Wireless accelerometer sensing of a self-sustained wireless integrated structural health monitoring (ISHM) system on

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications,Tsunami…

USDOT had been developed by the University of Maryland (UMD) and North Carolina State University (NCSU) with the URS (later named AECOM) Corporation [1]. This system, with remote sensing capability, is designed to be suited for fatigue condition assessment of highway steel bridges. Furthermore, the ISHM system would help in damage detection and deterioration diagnosis in early stages, predicting the remaining service life more accurately when compared with the traditional SHM system with reliable technology to improve current inspection

methods, and reduce the operating and maintenance costs.

number of wireless sensors along the bridge span are needed [1].

A scalable integral structural health monitoring (ISHM) system sponsored by the

The ISHM system based on wireless sensor networks entails a few recent innovations which applied the current state of the practice in remote sensing and highway infrastructure management. Accelerometers, in this system, are used for monitoring the vibration response of bridges so that the modal frequency information could be obtained and used to calibrate the finite element model of the monitored bridge. In this system, a new wireless piezoelectric sensor board had been designed and used. This board mainly consists of an 8-bit microcontroller, a FPGA, and a piezoelectric amplifier circuit. This device is enhanced with improved operating frequency and a four-wire, SPI-compatible interface while having lower power consumption. In the ISHM system, each single wireless sensor was tested on a shaker to verify that the developed sensor can recover the input information accurately. However, a single sensor could not catch enough data for structure monitoring and analysis. Thus, a

The example for the ISHM accelerometer monitoring case is the Structure No. 060025 Swing Bridge in Beaufort County, North Carolina (Figure 5, Beaufort #25 Bridge). The bridge consists of side spans and main spans. It should be noted that the structural support of the side span is a simply supported steel girder bridge, which has a relatively simple stress state compared with the main span because the

Beaufort #25 bridge, NC

Figure 5.

114

Sketch plan for monitoring system.

boundary condition of the main span is changed between simply supported and cantilever due to the close or open of the main span. Thus, the researchers of NCSU chose the main span as the targeted monitoring case for the dynamic behavior considering the complex stress states.

In this case, a row of smart sensors was attached to the bridge girders in the main span. The dynamic behavior was analyzed by data from accelerometers. Figure 6

Figure 7. The first three mode shapes from FE analysis.


In order to verify the reliability of the whole system, a field test for I-270 Bridge in Maryland by using this ISHM system was carried out with the accelerometer sensor locations shown in Figure 8. Figures 9 and 10 show the test results collected by

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

DOI: http://dx.doi.org/10.5772/intechopen.83500

The results of field test of I-270 bridge, MD: (a) the time-history data of sensor 3 and (b) the PSD of sensor 3.

these wireless sensors.

Figure 9.

Figure 10.

117

PSD of these sensors and the first mode shape of the bridge.

### Table 1.

Modal analysis results.

shows the test results of the bridge by using the set of wireless sensors. The data is processed using fast Fourier transform (FFT). The estimation of the natural frequency of the bridge about 4.0 Hz to 5.0 Hz was made by the NCSU researchers.

Meanwhile, the finite element model using the software ANSYS of Beaufort #25 Bridge was built and analyzed. The structural analysis was separated into two conditions due to the fact that the main span could swing. The first three mode shapes are illustrated in Figure 7, and the first five modes are summarized in Table 1. Depending on the relative amplitude of the mode shapes, these modes were noted as the vertical-dominated modes, the lateral-dominated modes, and the torsional-dominated modes (Figure 7).

The accelerometers are commonly used in highway bridges' monitoring for dynamic behavior. The monitoring results for the bridge are close to the finite element analysis result, and thus, the model was calibrated to be analyzed for other load conditions, and the test results were archived to be the baseline for future monitoring.

## 5. Case study 2: Remote monitoring of a self-sustained wireless integrated structural health monitoring (ISHM) system for highway bridges on I-270 bridge in MD

The second case study is under the same ISHM project [1] and was conducted by the University of Maryland at College Park. The types of sensors used in this project were (1) piezoelectric paint AE sensors; (2) wireless accelerometers; (3) laser sensor; (4) ultrasonic distance sensors; (5) BDI strain transducers; and (6) string pots.

Figure 8. Sensor locations.

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.83500

In order to verify the reliability of the whole system, a field test for I-270 Bridge in Maryland by using this ISHM system was carried out with the accelerometer sensor locations shown in Figure 8. Figures 9 and 10 show the test results collected by these wireless sensors.

Figure 9. PSD of these sensors and the first mode shape of the bridge.

Figure 10. The results of field test of I-270 bridge, MD: (a) the time-history data of sensor 3 and (b) the PSD of sensor 3.

shows the test results of the bridge by using the set of wireless sensors. The data is processed using fast Fourier transform (FFT). The estimation of the natural frequency of the bridge about 4.0 Hz to 5.0 Hz was made by the NCSU researchers. Meanwhile, the finite element model using the software ANSYS of Beaufort #25

First (torsional) 4.95 Hz First (torsional) 1.51 Hz Second (vertical) 5.92 Hz Second (torsional) 1.72 Hz Third (vertical) 6.74 Hz Third (vertical) 2.15 Hz Fourth (vertical) 7.48 Hz Fourth (vertical) 2.51 Hz Fifth (lateral) 8.18 Hz Fifth (vertical) 2.58 Hz

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications,Tsunami…

Bridge was built and analyzed. The structural analysis was separated into two conditions due to the fact that the main span could swing. The first three mode shapes are illustrated in Figure 7, and the first five modes are summarized in Table 1. Depending on the relative amplitude of the mode shapes, these modes were noted as the vertical-dominated modes, the lateral-dominated modes, and the

Fixed position Swing position

The accelerometers are commonly used in highway bridges' monitoring for dynamic behavior. The monitoring results for the bridge are close to the finite element analysis result, and thus, the model was calibrated to be analyzed for other load conditions, and the test results were archived to be the baseline for future

integrated structural health monitoring (ISHM) system for highway

The second case study is under the same ISHM project [1] and was conducted by the University of Maryland at College Park. The types of sensors used in this project were (1) piezoelectric paint AE sensors; (2) wireless accelerometers; (3) laser sensor; (4) ultrasonic distance sensors; (5) BDI strain transducers; and (6) string pots.

5. Case study 2: Remote monitoring of a self-sustained wireless

torsional-dominated modes (Figure 7).

bridges on I-270 bridge in MD

monitoring.

Figure 8. Sensor locations.

116

Table 1.

Modal analysis results.

## 6. Case study 3: Wireless structural monitoring of a newly replaced fiber-reinforced plastic (FRP) bridge deck

data to a local base receiver attached to a personal computer. In this load test, five boxes were linked in a "smart" network to control the data acquisition process. By using this system, the effort of instrumenting a bridge was reduced by more than half compared to hardwired systems. All CEA-06-250-UN350 uniaxial gages installed on the bridge are produced by the Measurements Group, Inc. As shown in Figure 13, strain gauges were strategically placed at different locations to measure strains due to live load effect. Three stringers, as shown in Figure 13(a), were load tested to check the distribution of live load over the stringers. Strain gages (data sets 2–1, 2–2, and 2–3 in Figure 13(b)) were located on the top of bottom flanges in the middle of the span. Comparison of finite element results and test results shows that the percentage difference ranged between 1.47 and 9.43%. The purpose of this test is to prove the integrated composite action between the steel stringers and the new FRP panels

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

DOI: http://dx.doi.org/10.5772/intechopen.83500

Truss bridge deck, stringers, strain sensor locations, and data: (a) plan, elevation, and section A-A views and

[10, 11].

Figure 13.

119

(b) section B-B and strain data measurement.

The use of FRP-composite bridge decks is viewed as a potential long-term solution for the concrete deck deterioration problem. A pilot project sponsored by the Federal Highway Administration (FHWA), USA, was undertaken by the Maryland State Department of Transportation, partnered with the University of Maryland to rehabilitate a steel truss bridge (MD24 over Deer Creek in Harford County, Maryland) using lightweight FRP deck [10, 11]. The existing steel truss bridge (Figure 11), built in 1934, carries two lanes of traffic, provides 9.14 m (30 ft.) of clear roadway, and is 37.50 m (123 ft.) long with severe roadway skew (Figure 12). The FRP deck panels are placed perpendicular to the stringers and act as a continuous plate between the stringer supports.

Load tests and structural monitoring were conducted to obtain information regarding the performance of the structure. For a relatively new material like FRP, the use of load tests can prove the structure's capacity. Wireless structural monitoring system developed through a previous FHWA small business innovation research (SBIR) contract to Invocon, Inc. in Conroe, Texas, was used. The system includes a data acquisition and communication nodes (Figure 13) connected to strain gages that can acquire data in digital form and relay the

Figure 11. Steel truss bridge on MD 24 over deer creek.

Figure 12. Replacement of a FRP deck panel.

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.83500

data to a local base receiver attached to a personal computer. In this load test, five boxes were linked in a "smart" network to control the data acquisition process. By using this system, the effort of instrumenting a bridge was reduced by more than half compared to hardwired systems. All CEA-06-250-UN350 uniaxial gages installed on the bridge are produced by the Measurements Group, Inc. As shown in Figure 13, strain gauges were strategically placed at different locations to measure strains due to live load effect. Three stringers, as shown in Figure 13(a), were load tested to check the distribution of live load over the stringers. Strain gages (data sets 2–1, 2–2, and 2–3 in Figure 13(b)) were located on the top of bottom flanges in the middle of the span. Comparison of finite element results and test results shows that the percentage difference ranged between 1.47 and 9.43%. The purpose of this test is to prove the integrated composite action between the steel stringers and the new FRP panels [10, 11].

#### Figure 13.

Truss bridge deck, stringers, strain sensor locations, and data: (a) plan, elevation, and section A-A views and (b) section B-B and strain data measurement.

6. Case study 3: Wireless structural monitoring of a newly replaced

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications,Tsunami…

solution for the concrete deck deterioration problem. A pilot project

The use of FRP-composite bridge decks is viewed as a potential long-term

sponsored by the Federal Highway Administration (FHWA), USA, was undertaken by the Maryland State Department of Transportation, partnered with the University of Maryland to rehabilitate a steel truss bridge (MD24 over Deer Creek in Harford County, Maryland) using lightweight FRP deck [10, 11]. The existing steel truss bridge (Figure 11), built in 1934, carries two lanes of traffic, provides 9.14 m (30 ft.) of clear roadway, and is 37.50 m (123 ft.) long with severe roadway skew (Figure 12). The FRP deck panels are placed perpendicular to the stringers and act as a continuous plate between the stringer

Load tests and structural monitoring were conducted to obtain information regarding the performance of the structure. For a relatively new material like FRP, the use of load tests can prove the structure's capacity. Wireless structural monitoring system developed through a previous FHWA small business innovation research (SBIR) contract to Invocon, Inc. in Conroe, Texas, was used. The system includes a data acquisition and communication nodes (Figure 13) connected to strain gages that can acquire data in digital form and relay the

fiber-reinforced plastic (FRP) bridge deck

supports.

Figure 11.

Figure 12.

118

Replacement of a FRP deck panel.

Steel truss bridge on MD 24 over deer creek.

## 7. Case study 4: Digital accelerometer monitoring of hanger cables on arch bridges

Arch-girder bridges with hanger cables are a popular type of bridges because they have the advantages of both arch and girder forms. Therefore, it is critical to check the performance of the hanger cables in order to guarantee road safety. The hanger, which ties the arch and the girder, is a key determinant of bridge quality. If one hanger is damaged, the whole structure is at risk. By detecting bridge's hangers, we may make judgment whether the bridge is in good condition or not:

$$T\_0 = ml^2 \left( 4.3865f\_1^2 - 0.2742f\_2^2 \right) \tag{9}$$

continuous vibrations above the steel frames during the discharge process. The vibration in the steel frames caused by the 76-in pipeline disturbance might lead to cracks in the original pipes below, steel fatigue, and joint failure. Once the above incidents occur, it has potential to result in the escape of poisonous gas, interruption of the production process, and even conflagration. In this case, accelerometer monitoring records are used to detect dynamic structural weaknesses of the steel frames, and then, the structural systems could be retrofitted to reduce the probable essential structure faults leading to industry disasters. Disturbances occur randomly along the 76-in pipeline due to vaporization of solid or liquid waste whose volume expands dramatically and raises the pressure in the pipeline. Waste flow also causes impact force on curved parts of the 76-in pipeline when the flow direction changes. The IMI 603C01 piezoelectric accelerometer is used in this case. It is a shearmode-type accelerometer with a ceramic sensing element. It is suggested that ceramic sensing elements provide great resolution and durability in noisy environments and it also covers both low-frequency and high-frequency measurements [15]. Fifty-six accelerometers in either vertical or horizontal direction are installed on the 76-in pipeline and the steel frame below. Accelerometers are aligned vertically along the 76-in pipeline and the steel frame since the response of the steel frame caused by disturbances could be monitored simultaneously by all sensors

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

With acceleration data from long-term monitoring, locations of vibrations and vibration levels could be identified. To provide methods to reduce vibration, the first step is to build the finite element model verified with monitoring data. In this case, SAP2000 is used to build the finite element model of the steel frame and the 76-in pipeline (Figure 17). By assigning time history recorded by the accelerometer, the fundamental frequency of the steel frame could be obtained by the FFT. The fundamental frequency of the steel frame could be also calculated by finite element

(Figure 16).

Figure 16.

121

Accelerometer installation and side view of design [14].

Figure 15.

76-in pipeline with steel frame and plan view [14].

DOI: http://dx.doi.org/10.5772/intechopen.83500

where T0 is the cable tension, m is the mass of the cable, l is the length of the cable, and f1, f2 are the first and second natural frequencies, respectively.

In Eq. (9), the stiffness of hanger cable is not needed to be tested, only frequencies. Therefore, it has an advantage of easy operation and usage. The demonstrated bridge here is a tied-arch bridge, and the above equation was used to calculate the cable forces, which are shown in Refs. [5, 12].

In the project, digital accelerometer JMM-268 dynamic testing instrument (Figure 14) was used to measure the first and second frequencies of hanger cables. When the frequencies were obtained, the hanger cable force can be calculated according to Eq. (9).

Comparing calculated hanger cable forces with cable force capacity, inspector of the bridge can locate critical sites and focus on those sites to do more detailed inspection. With the fast assessment method presented, only the first and second frequencies of the hanger cable need to be detected. This method was used to evaluate several arch bridges with hanger cables [5, 12].

Figure 14. Digital accelerometer JMM-268 dynamic testing instrument.

## 8. Case study 5: Accelerometer application on large steel frame structure

The steel frame structure is commonly used in the infrastructure of the petroleum industry to support numerous pipes and storage tanks. Vibration in steel frames is an industrial safety issue due to the movement of massive amounts of liquid, solid, and gas through the pipes. According to statistics published by David G. Maboney [13] regarding the causes of serious disasters in petrochemical industries, tube systems took up to 33% of the equipment. To identify the structural behavior of steel frames, accelerometers could be applied to detect vibrations. Shown here is an industry case that a new half-mile-long, 76-in (190 mm) diameter pipeline system is installed above a large 80-ft (24 m)-high steel frame structure in an oil refinery in Taiwan (Figure 15). The main function of the 76-in pipeline is to deliver massive amount of waste to the flare stack. However, an unexpected disturbance of the 76-in pipeline occurs which becomes the source of dramatic and

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures DOI: http://dx.doi.org/10.5772/intechopen.83500

Figure 15. 76-in pipeline with steel frame and plan view [14].

7. Case study 4: Digital accelerometer monitoring of hanger cables on

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications,Tsunami…

Arch-girder bridges with hanger cables are a popular type of bridges because they have the advantages of both arch and girder forms. Therefore, it is critical to check the performance of the hanger cables in order to guarantee road safety. The hanger, which ties the arch and the girder, is a key determinant of bridge quality. If one hanger is damaged, the whole structure is at risk. By detecting bridge's hangers,

2

where T0 is the cable tension, m is the mass of the cable, l is the length of the

In the project, digital accelerometer JMM-268 dynamic testing instrument (Figure 14) was used to measure the first and second frequencies of hanger cables. When the frequencies were obtained, the hanger cable force can be calculated

the bridge can locate critical sites and focus on those sites to do more detailed inspection. With the fast assessment method presented, only the first and second frequencies of the hanger cable need to be detected. This method was used to

8. Case study 5: Accelerometer application on large steel frame

The steel frame structure is commonly used in the infrastructure of the petroleum industry to support numerous pipes and storage tanks. Vibration in steel frames is an industrial safety issue due to the movement of massive amounts of liquid, solid, and gas through the pipes. According to statistics published by David G. Maboney [13] regarding the causes of serious disasters in petrochemical industries, tube systems took up to 33% of the equipment. To identify the structural behavior of steel frames, accelerometers could be applied to detect vibrations. Shown here is an industry case that a new half-mile-long, 76-in (190 mm) diameter pipeline system is installed above a large 80-ft (24 m)-high steel frame structure in an oil refinery in Taiwan (Figure 15). The main function of the 76-in pipeline is to deliver massive amount of waste to the flare stack. However, an unexpected disturbance of the 76-in pipeline occurs which becomes the source of dramatic and

In Eq. (9), the stiffness of hanger cable is not needed to be tested, only frequencies. Therefore, it has an advantage of easy operation and usage. The demonstrated bridge here is a tied-arch bridge, and the above equation was used to calculate the

Comparing calculated hanger cable forces with cable force capacity, inspector of

<sup>1</sup> � 0:2742 f

2 2 (9)

we may make judgment whether the bridge is in good condition or not:

<sup>T</sup><sup>0</sup> <sup>¼</sup> ml<sup>2</sup> <sup>4</sup>:<sup>3865</sup> <sup>f</sup>

cable forces, which are shown in Refs. [5, 12].

evaluate several arch bridges with hanger cables [5, 12].

Digital accelerometer JMM-268 dynamic testing instrument.

cable, and f1, f2 are the first and second natural frequencies, respectively.

arch bridges

according to Eq. (9).

structure

Figure 14.

120

continuous vibrations above the steel frames during the discharge process. The vibration in the steel frames caused by the 76-in pipeline disturbance might lead to cracks in the original pipes below, steel fatigue, and joint failure. Once the above incidents occur, it has potential to result in the escape of poisonous gas, interruption of the production process, and even conflagration. In this case, accelerometer monitoring records are used to detect dynamic structural weaknesses of the steel frames, and then, the structural systems could be retrofitted to reduce the probable essential structure faults leading to industry disasters. Disturbances occur randomly along the 76-in pipeline due to vaporization of solid or liquid waste whose volume expands dramatically and raises the pressure in the pipeline. Waste flow also causes impact force on curved parts of the 76-in pipeline when the flow direction changes.

The IMI 603C01 piezoelectric accelerometer is used in this case. It is a shearmode-type accelerometer with a ceramic sensing element. It is suggested that ceramic sensing elements provide great resolution and durability in noisy environments and it also covers both low-frequency and high-frequency measurements [15]. Fifty-six accelerometers in either vertical or horizontal direction are installed on the 76-in pipeline and the steel frame below. Accelerometers are aligned vertically along the 76-in pipeline and the steel frame since the response of the steel frame caused by disturbances could be monitored simultaneously by all sensors (Figure 16).

With acceleration data from long-term monitoring, locations of vibrations and vibration levels could be identified. To provide methods to reduce vibration, the first step is to build the finite element model verified with monitoring data. In this case, SAP2000 is used to build the finite element model of the steel frame and the 76-in pipeline (Figure 17). By assigning time history recorded by the accelerometer, the fundamental frequency of the steel frame could be obtained by the FFT. The fundamental frequency of the steel frame could be also calculated by finite element

Figure 16. Accelerometer installation and side view of design [14].

9. Conclusion

infrastructures.

Author details

USA

123

Chung C. Fu\*, Yifan Zhu and Kuang-Yuan Hou

\*Address all correspondence to: ccfu@umd.edu

provided the original work is properly cited.

The Bridge Engineering Software and Technology (BEST) Center, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD,

© 2019 The Author(s). Licensee IntechOpen. 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,

The purpose of the infrastructural monitoring is to have efficient use of the materials, energy, and labor to increase the performance of infrastructures. Advances of modern remote monitoring increase the efficiency, which is demonstrated in case studies. The emerging sensor technologies, no matter in situ, on-site, or airborne sensors, are increasingly used in the infrastructure sensing. An integrated structural health monitoring system (ISHM) includes the ability to extract information from sensor data to establish trends, such as the sensor signatures and structural damage, and make recommendation of actions to ensure the health of the

Utilization of Dynamic and Static Sensors for Monitoring Infrastructures

DOI: http://dx.doi.org/10.5772/intechopen.83500

Figure 17. Finite element model [16].

Figure 18. Research flowchart [16].

software. Therefore, the finite element model could be modified to increase accuracy of the model by comparing frequencies with data recorded by accelerometers. The higher-vibrated steel frame structure is suggested to increase stiffness by installing steel bracing or enlarging column size. Based on the modified finite element model, the effect of retrofitted design could be evaluated in software. After retrofitting, the improvement of the steel frame could be demonstrated by further accelerometers monitoring (Figure 18).

The accelerometer plays an important role in this industry case because it provides critical information for steel frame dynamic behavior due to unexpected turbulence. Based on the monitoring data, the accuracy of the finite element model could be enhanced. More accurate models can help structural engineers figure out effective methods to reduce vibration which potentially leads to serious industrial disasters. The improvement could also be validated by further monitoring using accelerometers. On the other hand, steel frame vibration caused by the 76-in pipeline turbulence is also related to the volume of waste delivered to the flare stack. Therefore, the safe range of waste consumption could be determined to avoid insecure vibrations of the steel frame.

## 9. Conclusion

The purpose of the infrastructural monitoring is to have efficient use of the materials, energy, and labor to increase the performance of infrastructures. Advances of modern remote monitoring increase the efficiency, which is demonstrated in case studies. The emerging sensor technologies, no matter in situ, on-site, or airborne sensors, are increasingly used in the infrastructure sensing. An integrated structural health monitoring system (ISHM) includes the ability to extract information from sensor data to establish trends, such as the sensor signatures and structural damage, and make recommendation of actions to ensure the health of the infrastructures.

## Author details

software. Therefore, the finite element model could be modified to increase accuracy of the model by comparing frequencies with data recorded by accelerometers. The higher-vibrated steel frame structure is suggested to increase stiffness by installing steel bracing or enlarging column size. Based on the modified finite element model, the effect of retrofitted design could be evaluated in software. After retrofitting, the improvement of the steel frame could be demonstrated by further

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications,Tsunami…

The accelerometer plays an important role in this industry case because it provides critical information for steel frame dynamic behavior due to unexpected turbulence. Based on the monitoring data, the accuracy of the finite element model could be enhanced. More accurate models can help structural engineers figure out effective methods to reduce vibration which potentially leads to serious industrial disasters. The improvement could also be validated by further monitoring using accelerometers. On the other hand, steel frame vibration caused by the 76-in pipeline turbulence is also related to the volume of waste delivered to the flare stack. Therefore, the safe range of waste consumption could be determined to avoid

accelerometers monitoring (Figure 18).

Figure 17.

Figure 18.

122

Research flowchart [16].

Finite element model [16].

insecure vibrations of the steel frame.

Chung C. Fu\*, Yifan Zhu and Kuang-Yuan Hou The Bridge Engineering Software and Technology (BEST) Center, Department of Civil and Environmental Engineering, University of Maryland, College Park, MD, USA

\*Address all correspondence to: ccfu@umd.edu

© 2019 The Author(s). Licensee IntechOpen. 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.

## References

[1] Fu CC, Yunfeng Zhang, Yuan FG. Development of a Self-sustained Wireless Integrated Structural Health Monitoring (ISHM) System for Highway Bridges [Internet]. 2011. Available from: http://ishm.umd.edu/ index.php [Accessed: June 10, 2018]

[2] Hsieh KH, Halling MW, Barr PJ. Overview of vibrational structural health monitoring with representative case studies. Journal of Bridge Engineering. 2006;11(6):707-715

[3] Farrar CR, Baker WE, Bell TM, Cone KM, Darling TW, Duffey TA, et al. Dynamic Characterization and Damage Detection in the I-40 Bridge Over the Rio Grande. NM (United States): Los Alamos National Lab.; 1994. DOI: 10.2172/10158042. Web

[4] Bendat JS, Piersol AG. Engineering Applications of Correlation and Spectral Analysis. New York: Wiley-Interscience; 1980. 315 p

[5] Fu CC, Wang S. Computational Analysis and Design of Bridge Structures. USA: CRC Press; 2014. ISBN-13 978-1466579842

[6] California Department of Transportation (Caltrans). Available from: http://www.dot.ca.gov [Accessed: June 10, 2018]

[7] Michael Minor & Associates (Oregon, USA). Available from: http:// www.drnoise.com [Accessed: November 9, 2018]

[8] Andrews J, Buehler D, Gill H, Wesley L. Transportation and Construction Vibration–Guidance Manual, (CT-HWANP-RT-13-069.25.3). CALTRAN; 2013

[9] Wikipedia. Acceleration [Internet]. Available from: https://en.wikipedia.org/ wiki/Acceleration#/media/File:1-D\_kine matics.svg [Accessed: June 10, 2018]

[10] Fu CC, Alayed H, Amde AM. Field performance of the fiber reinforced polymer (FRP) deck of a truss bridge. United States: Journal of Performance of Constructed Facilities, USA: ASCE. 2007;21(1 Jan/Feb):53-60

[11] Fu CC. Load Test Report—Bridge No. 12016 on MD-24 over Deer Creek (FRP Bridge Deck Replacement) to Maryland State Highway Administration and Federal Highway Administration IBRC project. Rev. 2. 2003. Available from: https://cpb-us-e1. wpmucdn.com/blog.umd.edu/dist/f/ 392/files/2016/08/MD-24-Report\_ R2-2egkdtr.pdf [Accessed: June 10, 2018]

[12] Li X, Sun M, Fu CC. Fast assessment of hanger cables on arch-girder bridges. In: Bridge Maintenance, Safety, Management and Life Extension Chapter 210. London: CRC Press/Taylor & Francis Group. 2014. pp. 1527-1534

[13] Mahoney D. Large property damage losses in the hydrocarbon-chemical industries: A thirty-year review. M & M Protection Consultants; 1997

[14] Yi-Chen Z, Tsung-Chin H. A field study of dynamic measurement and security assessment of large-scale petrochemical structures [thesis]. Taiwan: Civil Engineering, National Kaohsiung University of Science and Technology; 2013

[15] PCB. Model 603C01 Platinum Lowcost Industrial ICP® Accelerometer Installation and Operating Manual; 2010

[16] Tsung-Hsuan L, Tsung-Chin H. Dynamic simulation and strengthening analysis of large steel frame structure [thesis]. Taiwan: Civil Engineering, National Cheng Kung University; 2014

**125**

**Chapter 7**

**Abstract**

**1. Introduction**

areas of the sea floor subside.

Risk Mapping

*Abu Bakar Sambah and Fusanori Miura*

Geo Spatial Analysis for Tsunami

Tsunami risk is a combination of the danger posed by tsunami hazard, the vulnerability of people to an event, and the probability of destructive tsunami. The spatial multicriteria approach made a possibility for integrating the vulnerability and risk parameters to assess the potential area that will be affected by the tsunami. The study applied the parameters of physical and social vulnerability and combined element at risk to assess tsunami risk in the coastal area of East Java Indonesia. All parameters in both tsunami vulnerability and tsunami risk assessment were analyzed through cellbased analysis in geographical information system. The weight of each parameter was calculated through the analytical hierarchy process. The results were provided as maps of tsunami vulnerability and tsunami risk. Tsunami risk map described five classes of risk. It described that coastal area with a low elevation and almost flat identified as high risk to the tsunami. The coastal area with a high density of vegetation (mangrove) was defined as the area with low level of tsunami risk. The existence of river and other water canals in coastal area was also analyzed for generating tsunami risk map. Risk map highlights the coastal areas with a strong need for tsunami mitigation plan.

**Keywords:** tsunami, vulnerability, risk, geospatial, weighted overlay, GIS

Tsunami can be defined as a series of waves created by an impulsive disturbance in the water body. It causes severe damage to coastal areas. A tsunami wave could be less than 1 m high in the open ocean and traveling at up to 800 km/h in which the wave energy will be extended from the surface to the ocean floor. The wave energy of tsunami will be compressed into a much shorter distance when it approaches the coast, creating potentially large destructive to the coastal areas [1]. A tsunami can be generated when the sea floor abruptly deforms and a bottom layer of water body displaces the overlying water vertically. One kind of earthquake that is related to the crustal deformation of the earth is tectonic earthquakes. When these earthquakes happen in the bottom of the sea, the water layer above the deformed area is displaced from its equilibrium position. Waves are formed as the displaced water mass, which occurs due to the impact of gravity. A tsunami can be generated when large

In the deep water of the open ocean, the speed of tsunami waves can be up to 800 km/h. The energy wave of tsunami will decrease dramatically when it approaches the coast, but its height can be 10 times or more and have catastrophic consequences to the coastal areas. As a result, the low-lying areas of the coast and the areas near

## **Chapter 7**

References

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[10] Fu CC, Alayed H, Amde AM. Field performance of the fiber reinforced polymer (FRP) deck of a truss bridge. United States: Journal of Performance of Constructed Facilities, USA: ASCE.

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