A Wireless Sensor NetworkBased Structural Health Monitoring System ...

Computer-Aided Civil and Infrastructure Engineering 28 (2013) 193–209

A Wireless Sensor Network-Based Structural Health Monitoring System for Highway Bridges Xiaoya Hu∗ & Bingwen Wang Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

& Han Ji School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan 430074, China

Abstract: An integrated structural health monitoring (SHM) system for highway bridges is presented. The system is based on a customized wireless sensor network platform with a flexible design that provides a variety of sensors typical in SHM. These sensors include accelerometers, strain gauges, and temperature sensors with ultra-low power consumption. An S-Mote node, an acceleration sensor board, and a strain sensor board are developed to satisfy the requirements of bridge structural monitoring. Communication software components are integrated within TinyOS operating system to provide a flexible software platform whereas the data processing software performs analysis of acceleration, dynamic displacement, and dynamic strain data. The prototype system comprises a nearly linear multi-hop topology and is deployed on an in-service highway bridge. Data acquired from the system are used to examine network performance and to help evaluate the state of the bridge. Experimental results show that the system enables continuous or regular interval monitoring for in-service highway bridges.

1 INTRODUCTION Structural health monitoring (SHM) has become an increasingly important technology to determine the static and dynamic responses of civil infrastructure ∗ To

whom correspondence should be addressed. E-mail: huxy@mail. hust.edu.cn.

C 2012 Computer-Aided Civil and Infrastructure Engineering. DOI: 10.1111/j.1467-8667.2012.00781.x

to environmental conditions or vehicle loads during construction or while in service. By calculating, comparing, and analyzing these responses, SHM can serve as an emergency alert system and can provide safety assessment for maintenance decisions and structural damage identification, among other purposes (Ou, 2003; Lynch and Loh, 2006). From the perspective of structural state monitoring, the general approaches to bridge SHM are load rating, experimental monitoring or random loading, regular interval monitoring. The former provides operational load ratings that quantify the load-carrying capacity of in-service bridges, and it enables the assessment of the quasi-static responses of these bridges. However, load ratings require temporary closure of bridges and procedural loading with the use of vehicles with known weights. This approach is limited by the periodic, schedule-based, and high-cost nature of the application. Furthermore, load ratings reflect only a particular state under a given condition, and data do not have the desired statistical properties. These attributes contribute to difficulty in the assessment of actual bridge characteristics. Therefore, a continuous monitoring system is needed to determine structural conditions accurately. The latest continuous and random load approach involves the use of sensor networks based on embedded optical sensors hardwired into a data acquisition system. This method can conduct continuous structural monitoring of in-service bridges under random loads for long periods. However, it suffers from several drawbacks, including (1) inapplicability to old bridges because of the requirement for

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pre-embedded sensors, (2) high installation costs, and (3) delicate optical sensors that are easily damaged during construction. Advancements in wireless sensor network (WSN) technology (Akyidiz et al., 2002) in the past decade have provided opportunities for continuous monitoring in bridge SHM applications. Compared with cable-based sensor networks, WSNs provide wireless continuous monitoring at a considerably lower cost. WSNs also facilitate simple yet convenient deployment and monitoring. However, data transmission based on WSN technology has several limitations, including longdistance transmission, limited transmission bandwidth, and limited power supply. To address these issues, recent studies (see Section 2) have generally used one of two approaches: one-hop network topology with short transmission or multi-hop topology transmission of sensor data. In a one-hop monitoring system, realtime transmission is relatively simple. However, most long-span bridges require multi-hop communication, as the distance of these bridges exceeds the broadcast domain of one hop. For large-scale bridges that require in-service assessment, multi-hop topology may be essentially used in WSN for SHM. Due to the limited capacity of battery power supplies, power efficiency is also an important consideration to satisfy the requirements of bridge SHM and thus extend system lifetime. Using WSN technology, we develop an integrated bridge health monitoring system based on a customized, multi-hop, low-power platform. This system can be applied to continuous or regular interval monitoring under random loads. The major contributions of this work to WSN applications for SHM are as follows: 1. A customized monitoring platform with a tailored S-Mote, an acceleration sensor board, and a strain sensor board is developed. The flexible and scalable system architecture enables the system to support a wide array of sensing tasks for SHM. 2. Combination of data analysis software and data collection platform in the integrated bridge monitoring system is presented for continuous monitoring, in which all kinds of dynamic response parameters (including vibration acceleration, dynamic displacement, and dynamic strain) can be determined and analyzed. 3. Instead of simulations in a laboratory testbed, testing with deployment under complex conditions is conducted on an in-service highway bridge. The test may provide experiential knowledge on solving problems in the practical application of WSNs.

2 RELATED WORK Ou (2003) summarized the contents and functions of an SHM system. The SHM system for civil infrastructures typically includes data acquisition, transmission, and processing units, as well as data management systems. Damage detection and decision making are performed regularly with the use of data measured by an SHM system. Since the early 1980s, extensive research has been devoted to damage identification methods for structures. These methods can be grouped into two categories according to whether the studied performance index has static or dynamic parameters. Static index method (Garcia et al., 2008; Caddemi and Morassi, 2007) identifies damage based on static displacement or static curvature. Significant findings have also been obtained through dynamic index method (Carden and Brownjohn, 2008; Soyoz and Feng, 2009). These damage assessment methods commonly use modal parameters, such as natural frequency, damping ratio, and mode shape curvature, to construct damage identification models. Various computational tools, such as wavelet analysis (Pakrashi et al., 2007; Jiang et al., 2007; Umesha et al., 2009), neural networks (Jiang and Adeli, 2005, 2007), fuzzy logic (Adeli and Jiang, 2006), and genetic algorithms (Marano et al., 2011), are also often introduced to improve damage detection accuracy. Dynamic fuzzy wavelet neural network approaches for damage detection and control algorithms have also been developed (Jiang and Adeli, 2008a, b). Park et al. (2007) presented a novel approach, that is, the use of terrestrial laser scanning for SHM. Ceriotti et al. (2009) presented a hardware/software solution to monitor Torre Aquila, in which the hardware core is based on TMote-like devices, and the software is based on TeenyLIME, a WSN middleware. Damage detection and identification technology can evaluate structure safety and recommend repairs in advance. However, the accuracy and the reliability of damage identification largely depend on the time histories data gathered by the SHM system. To reduce the cost of installation and monitoring, researchers are working to develop novel methods of data acquisition in a nondestructive manner. Several WSN systems have been proposed for bridge SHM. Xu et al. (2004) discussed a WSN system to collect data on structural vibration. The authors presented reliable data transport over a multihop network based on Mica2 motes, but the system was tested only in a small-scale indoor testbed. RuizSandoval et al. (2006) developed an acceleration board and a strain board using Mica motes for communication and control, but the system relies on one-hop wireless communication, which is not scalable to large structures.

A wireless sensor network-based SHM system for highway bridges

Furthermore, the system was not tested under harsh environmental conditions. Lynch et al. (2005) deployed 14 wireless sensors to monitor the forced acceleration responses of Geundang Bridge in Korea. However, their system is also of single-hop network type. Pakzad et al. (2005, 2008), Kim (2005), and Kim et al. (2007) proposed a 46-hop WSN, which can measure ambient vibrations. They developed an acceleration board based on a MicaZ mote with abundant on-chip peripherals. Although these studies reflect significant progress in the development of WSNs for SHM, the systems proposed rely on a series of commercial Mica motes. With the relatively high power consumption and the restricted sensor interface of Mica motes, these motes are restricted to continuous monitoring application for bridge SHM, in which power consumption is a crucial issue. Whelan and Janoyan (2009, 2010) recently addressed strain- and vibration-based low-power monitoring by a WSN. The authors described a customized data acquisition platform (called WSS) based on Tmote Sky Mote, which conducts strain- and vibration-based monitoring through a switch. Whelan et al. (2009, 2011) deployed WSS on RT345 Bridge over Big Sucker Brook and used vibration measurements for operational modal analysis. Gangone et al. (2011) discussed the load testing and the rating of a simply supported bridge, in which WSS from strain measurements was deployed. This approach can provide complete information for SHM. However, the large-scale bridge communication issue in bridge SHM could not be addressed because the system relies on single-hop, star-topology wireless communication between sensors and base stations. Bocca et al. (2009, 2011a,b) introduced a time-synchronized WSN and a WSN with embedded Goertzel algorithm to process acceleration data locally and in real time, respectively. In their work, the network is based on ISMO-2 nodes and organized into a star topology to enable accelerometer measurements in a wooden model bridge. Similarly, the single-hop topology of their system could not satisfy the requirements of large-scale bridge structural monitoring. By contrast, this study presents an integrated bridge health monitoring system based on a customized WSN platform. The platform is suitable for monitoring static and dynamic structural response parameters (including vibration acceleration, dynamic displacement, and dynamic strain) under random loads and is thus capable of continuous monitoring. The designed S-Mote improves flexibility in connecting different sensor boards or in offering communication functions. The platform can collect all kinds of sensor signals, such as acceleration, temperature, and strain signals. The radio power required for single-hop communication in a large network topology is impractical, but network communication in the

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proposed system addresses this problem with the use of multi-hops, which are essential for scalable deployment.

3 CUSTOMIZED MONITORING PLATFORM Five major prerequisites for WSN platforms exist in bridge health monitoring applications. These requirements are as follows: 1. Physical quantities such as acceleration, temperature, and strain should be collected. Therefore, different types of sensor boards that supply multiple sensor interfaces are essential platform components. 2. The measurement of ambient vibrations caused by wind and traffic requires a sufficiently sensitive accelerometer range. This requirement entails an acceleration sensor board that can detect weak vibration signals with peak amplitudes as low as 2 mg. The board should have a low-noise design and should take noise-reducing measurements for weak signal sampling and analog-to-digital converters (ADC), among others. In addition, strain sensors require calibration and temperature compensation before use. 3. Long-term installation of nodes on a bridge is a requirement in bridge health monitoring. Thus, low power consumption is a crucial feature of the platform. 4. Bridges typically have multiple spans, and their total lengths always exceed the broadcast domain of a node. Therefore, applications demand multi-hop wireless communication. 5. Time synchronization is required to perform correlation analyses of sampled data. Two methods are generally used in designing an optimal WSN platform: the use of standard motes and the use of a customized platform. The first method uses commercially available motes, which are convenient and lend themselves to rapid development. However, the use of motes may restrict the performance and the operation of the platform. By contrast, the second method involves the development of a customized platform, which entails a long design period. Nevertheless, the platform is more flexible and efficient. In consideration of these features, this study proposes a multi-hop WSN-based platform with a customized mote for continuous online SHM. The monitoring platform consists of multiple nodes and a base station. Each node has an S-Mote, either an acceleration sensor board or a strain sensor board, and two 3.6 V lithium batteries (Figure 1). The power system comprises lithium batteries and a monitoring

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S-mote node

batteries

Acceleration sensor board batteries

Strain sensor board

S-mote node (a)

(b)

Fig. 1. WSN platform with (a) acceleration board, (b) strain board.

troller unit provides the computational core of the platform, whereas the RF module performs wireless communication. The sensor expansion pins provide the interfaces via a sensor board, and the power management unit supplies power to the other modules. Low power consumption is imperative for long-term wireless deployment because power supply sources determine system life. After a comparison and an evaluation of existing products such as Atmel, Motorola, and Microchip, MSP430F1611 ultra-low-power microcontroller was selected for S-Mote. Table 1 shows that the microcontroller consumes only 2 mA of nominal current. Doze mode can reduce consumption to 1 μA, whereas ATmega128 (L) consumes 8 mA and 20 μA. The MSP430F1611 microcontroller runs on 1.8–3.6 V. When running at 1 MHz with a supply voltage of 2.2 V, the microcontroller consumes 330 μA current in active mode; off-mode operation reduces consumption to 0.2 μA. MSP430F1611 provides the largest on-chip (10 KB) RAM buffer, 48 KB of flash memory, an integrated 12-bit ADC, and a maximum conversion rate greater than 200 kbps. S-Mote has a 16-pin IDC expansion header for connecting sensor boards. Through exportation of I2C, UART, AD, and Digital I/O over the expansion header, expanders can be used to attach different kinds of sensor boards. S-Mote uses the Chipcon CC2420 radio in 2.4 GHz band, a wideband radio with

Fig. 2. S-Mote node.

circuit for battery voltage. The sensor nodes form a linear topology that measures acceleration, strain, or other signals with the use of data transmitted to a base station over multi-hop routing. The base station, which is connected to a host computer via USB interface, transfers data to the upper data analysis software. The base station is a server that supplies more computational power and that has a larger storage than a sensor node. It can also connect to the Internet. 3.1 Hardware design of the platform 3.1.1 S-Mote. S-Mote (Figure 2), which is designed for SHM applications, is composed of four modules: microcontroller unit, RF module, power management unit, and sensor expansion pins. Figure 3 shows the schematic of these major components. The microcon-

Sensor Expansion RFModule

MCU

Power Management

Fig. 3. Block diagram of the S-Mote components.

Pins


Table 1 Comparison of the operating parameters of different microcontrollers ATmega128 ATmega128L MSP430F1611 Flash memory RAM on-chip Integrated ADC Normal-mode current (mA) Doze-mode current (μA)

128 KB 4 KB 16-bit

128 KB 4 KB 16-bit

48 KB 10 KB 12-bit

8

8

2

20

20

1

0-QPSK modulation, and a DSSS at 250 kbps. The high data rate facilitates shorter active periods and thus further reduces energy consumption. In addition, the CC2420 chip transceiver features one of the lowest power consumption specifications in the IEEE802.15.4 family of modems. The receiving state consumes 19.7 mA, and the transmission state consumes 17.4 mA at a receiving sensitivity of –99 dBm. S-Mote can also communicate with any number of devices that share the same physical layer. These devices include those from other vendors because CC2420 supports IEEE 802.15.4 protocol. An internal 2.4 GHz planar, inverted, folded antenna built into the printed circuit board is used for S-Mote, which is programmed through an on-board USB that also supplies power. S-Mote is equipped with low-power temperature and humidity sensor SHT10 by Sensirion (Sensirion AG, Seaefa, Switzerland) to measure environmental parameters. The sensor has 4.5% RH and 0.5 ◦ C degree of accuracy. The power consumption of the sensor is 3 mW in active mode and 2 μW in sleep mode under 3.3 V power supply voltage. Five measures for energy conservation in S-Mote are used to reduce power consumption in WSNs for scalable monitoring: 1. Other components (such as CC2420) that can also function at low voltages are chosen, that is, those that can run on two AA batteries or one lithium battery. 2. A CPU that can be switched off separately is used, which allows the chip peripherals to work independently. 3. The frequency at which the core task is conducted is reduced to lessen the power consumption of the system while ensuring operational speed. Limiting frequency is essential because the operating power of the CMOS circuit comes primarily from charge

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and discharge to the capacitor of the next input terminal when the switch changes. 4. Interrupt technology is used to keep the system in sleep mode regularly. 5. The CPU controls the power of the external IC so that it can be turned off when no work is being done. 3.1.2 Design of the acceleration sensor board. Accelerations provide useful information on structural vibration characteristics. For the collection of structural vibration signals, a new accelerometer board is designed (Figure 1a), in which a two-dimensional Silicon Designs 1221L accelerometer is used. This accelerometer is a low-noise integrated accelerometer, with the input acceleration ranging from –0.1 g to +0.1 g. It provides the monitoring system with acceptable sensitivity to ambient structural vibrations at a relatively low cost. Tests show that the accelerometer has a noise floor of √ 32 μg/ Hz, which is small enough to enable resolution of the ambient vibrations of most structural systems (Kim et al., 2007). The sensitivity of SD1221L is 2,000 mV/g, which corresponds to a peak bridge vibration of 2 mg. The output of the accelerometer is 4 mV, which is considered a weak signal. The signal needs differential amplification, which is a requirement in bridge structural monitoring. The magnification is set at 500 at a range from 0–4 mV to 0–1 V to ensure wide-range data collection input. At the same time, multilevel amplifier technology is adopted to avoid the self-excited oscillation caused by excessive magnification (Figure 4). First, the first-level differential amplifier magnifies the signal 50 times. A high magnification may cause system noise, so a lowpass filter with a cutoff frequency of 50 Hz is attached behind the amplifier. The second-level amplifier then magnifies the signal 10 times. For the low-pass filter, a two-order Butterworth filter is used for convenience. Finally, the output signal of the acceleration sensor board is fed into the 12-bit ADC in S-Mote, which is set with an external 2.5V reference. As implemented in the design, the system with acceptable sensitivity accelerometer and 12-bit resolution ADC is able to detect ambient vibrations due to wind and traffic on the order of hundreds of micrograms. Higher sensitivity accelerometer and higher resolution ADC would further improve the resolution. Furthermore, changes in temperature may cause the accelerometer output to drift, so temperature compensation is required (Kim, 2005), especially when the temperature varies over a wide range. Linear forecast amendment method is adopted to compensate for the effect of temperature. For example, the accelerometer output Vg contains two components: one under

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Accelerometer

Differential Amplifier

Low-pass Filter

Sencond-lever

S-Mote

Amplifier

Fig. 4. Multilevel amplifier and filter of vibration signals.

acceleration Vg and the other is affected by temperature Vt ·Vg = Vg + Vt = kg T + Vg , where kg is the slope, and T is the temperature variable. Therefore, when finding a variable Vt as follows Vt = −kg T + Vb, where Vb is the offset, Vt can be used to compensate for Vg . 3.1.3 Design of the strain sensor board. Aside from acceleration, structural strain is also an important physical quantity. Strain measurement can directly reflect the extent of damage, so it is a critical component of SHM. Figure 1b shows the new strain sensor board developed in this work. Many different strain sensors that use diverse measurement methods, including mechanical, optical, and electrical techniques, are available. A mechanical strain sensor, such as a slide caliper, is simple, but generally, it has poor resolution. A device with levers to amplify strain to readable values can be developed, but this tool is prohibitively large. Optical sensors are typically costly or too delicate for use due to the large number of S-Motes expected for deployment on a bridge. Electrical sensors, which include piezoelectric sensors, semiconductor strain gauges, and the widely used foil strain gauges, have high resolutions and are compact, durable, and inexpensive. Therefore, electrical sensors are good candidates for the development of wireless strain sensor boards. In electrical sensors, the sensitivity of piezoelectric sensors in low frequency range is relatively poor; that of semiconductor strain gauges to temperature variation and their tendency to drift are nontrivial disadvantages in view of these gauges’ DC capability (Ruiz-Sandoval et al., 2006). By contrast, foil strain gauges have a wide frequency range and possess DC capability. They also cost low and can be installed easily on structural surfaces as opposed to being embedded in concrete. Thus, the foil strain gauge is chosen for this study. However, environmental factors, particularly temperature, easily deform strain gauges. Therefore, an asymmetrical bridge circuit configuration (Figure 5) is developed to address this issue. In the figure, kR0 denotes a precise resistance, R0 (1 + X1 + X2 ) represents the strain gauge attached along the direction of bridge compressive stress, and R0 (1 + X1 ) is vertically located at the position of R0 (1 + X1 + X2 ), where X1 and X2 are the deformations caused by changes in environment

R0(1+X1)

R0(1+X1+X2)

V KR0 KR0

V0 Fig. 5. Temperature compensation circuit.

temperature and bridge stress, respectively. Because R0 (1 + X1 ) does not have bridge stress, the strain gauge suffers only from temperature deformation, which is exactly the same as that of R0 (1 + X1 + X2 ). For the circuit shown in Figure 5, K X1 , X2 , input V 0 is estimated to be related only to X2 , and the influence of temperature deformation X 1 is eliminated. In the system, K has a value of 1,000, with which acceptable compensation effects can be achieved. In the strain sensor board, the strain signal is obtained with the above-mentioned temperature compensation circuit. Hence, the output of the bridge circuit has the same signal processing as the vibration signal. This process includes differential amplification, low-pass filtering, and second-level amplification. Finally, the processed signal is fed into an ADC. A target noise level is sought that is equal to 1 με. Significant high frequency noise was found to be present in the strain sensor board. The low-pass filter described earlier was also employed in this application to remove the high frequency noise. This filter also reduces the problem of aliasing. 3.1.4 Power consumption. The power consumption of a system is one of the most critical issues in WSNs. Therefore, the limited power available in sensor nodes should be used efficiently. The performance of the nodes in each operation affects the life spans of the nodes and subsequently, the lifetime of the entire network. Table 2 lists the power consumption values of the platform, as determined by laboratory measurements. In the experiments, two AA lithium batteries provide a total capacity of 7,500 mAh, which is more powerful than that supplied by conventional AA batteries. Each system activity covers synchronization, sampling, data transmission to the sink node, and sleep mode until the


Table 2 Power consumption in various operational situations (7.2 V supply voltage) Operation (S-Mote + acceleration board) Sleep mode Idle mode Sampling Radio packet transmission Radio listening/packet reception

[mA] ∼2 μA 9.6 13.4 34.8 36.7

Operation (S-Mote + strain board) Sleep mode Idle mode Sampling Radio packet transmission Radio listening/packet reception

[mA] ∼2 μA 9.8 14.1 35.2 37.0

next activation. During idle periods, the sensor board and the mote are turned on but do not perform any operation. Table 2 also shows that the data transmission and the reception by the radio are the key factors for power consumption. These values are used to estimate the expected lifetime of the proposed SHM system. In the current prototype, the two AA lithium batteries with 7,500 mAh capacity power the nodes, and they provide a supply of 7.2 V at full charge. Consequently, if about 90% of the battery resources are used before they are discharged below the operational voltage required by the nodes, an effective capacity of about 6,750 mAh results in a continuous monitoring service life of about 168 hours or 7 days, as dictated by battery resources. In SHM application, a duty-cycle approach to sampling is adopted. Therefore, reducing the sampling period prolongs the service life of the system. When a duty-cycle approach is implemented, in which the sensor node participates in active sampling for only 1 hour/day, the service life is extended to 168 days.

3.2 Platform software design 3.2.1 Software architecture for S-Mote. TinyOS is used in S-Mote. It is an operating system developed by UC Berkeley and has been adopted by numerous WSN research groups. TinyOS is a multilevel componentoriented software that supports a wide variety of applications for WSNs. Low-level components perform basic tasks, and high-level components use the sequences of the low-level components to achieve a more complex functionality while maintaining coding efficiency and simplicity. The system software architecture intended to satisfy the requirements of bridge SHM is illustrated in Figure 6. The figure shows that the main components of TinyOS include peer-to-peer communication, routing, reliable data transfer, time synchronization, and data buffering.

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3.2.2 Workflow of S-Mote. The program installed in S-Mote collects the sampled data, writes these data into flash memory, and transmits them to the base station via routing protocol. In the present system’s platform, MintRoute protocol (Woo et al., 2003) is used. MintRoute establishes packet routing information by minimizing the power cost of multi-hop travel from the sensor nodes to the base station, so it is applied to the platform to satisfy the demand for low-power consumption. Figure 7 shows the workflow of S-Mote. The green LED light is turned on after the S-Mote nodes are deployed and switched on. The light indicates that the nodes are ready and waiting for the start command from the base station. As soon as the nodes receive the start command, they collect ambient vibration data or strain data, and they write these back to the flash memory with the blue LED light on. After the sampling period, the nodes switch to sleep mode with the blue LED light off. The node reads the data reserved from the flash memory and transports these back to the base station when it receives the send data command. 3.2.3 Time synchronization protocol. In our system, the Energy-Balanced time Synchronization (EBS) protocol is used for time synchronization. Compared with time-stamping Flooding Time Synchronization Protocol (FTSP), the EBS protocol considers the special requirements and characteristics of bridge SHM, such as energy balance. The protocol achieves energy-balanced performance by exploiting a dynamic span leader election algorithm while reducing the number of broadcast synchronization messages. A more in-depth examination of the protocol can be found in the recent work of Hu et al. (2010). This section provides only a brief introduction. EBS defines network topology in terms of hierarchy level. A span is a level and a broadcast scale. In each span, a node is selected as the time-stamped message sender and has maximum residual energy, whereas the other nodes are receivers in the same span. The sender node of each span is dynamically elected in every synchronization period. EBS synchronizes the time of a sender with that of possibly multiple receivers with the use of a single radio message time-stamped as FTSP at both the sender and receiver sides. In theory, the synchronization process can be described as follows: Step 1: The root node broadcasts a synchronization packet to inform the nodes of the first span in which they should initiate the synchronization. Each synchronization packet contains three fields: packetType, timestamp, and rootID. Step 2: The sensor nodes (IDij ) that receive the synchronization packet first compare the rootID with their IDij . If i(IDij ) > i(rootIDij ), they discard the synchronization packet. Otherwise, the nodes estimate their

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Application Layer Component

Reliable Data

(SHM)

Time

Transfer

Synchronization

Component

Component

Routing Component Data Buffer Peer- to- Peer Communication Component

Component

Fig. 6. Software architecture of the TinyOS components.

Begin

Nodes wait for command N Is it the command from base station? Y Parse command

N

Y Start sampling?

Sampling data and writing into Flash

N Send data command for this node

Y Blue LED off

Flash is full?

N Reading data in Flash, data transmitted back

Y Reading Flash over?

Green LED on

Fig. 7. Workflow of S-Mote.

own local clock offset and modify the clock according to the received timestamp. After waiting for a bounded random duration, they return the acknowledged ACK packets. Step 3: The root node compares the remaining energy after all the ACK packets have been received, and it selects the node with the maximum remaining energy as the next synchronization node. When multiple nodes have the same remaining energy, an arbitrary node is elected. Then the root node sends the select packet to the selected node. Step 4: The node selected as the synchronization node broadcasts a synchronization packet to all the nodes in the same span. The other nodes directly discard the select packet. The next synchronization period starts from Step 1.

The proposed EBS algorithm follows the notion of fine-grained clock, MAC-layer time-stamping with several jitter-reducing techniques to achieve high precision ´ et al., 2004). The average error of the in FTSP (Maroti algorithm for a single-hop case is exactly the same as FTSP. For a sampling rate of 100 Hz, a total jitter of 500 μs or 5% of the sampling interval was selected as ´ et al. the cap for the total jitter. Experiments by Maroti (2004) showed that the protocol limits the spatial jitter to 67 μs over a network of 59 nodes and 11 hops. The tests in the study of Kim et al. (2007) indicated that the temporal jitter is limited to 10 μs for a sampling rate of up to 6.67 kHz. In this study, the system is a network of 26 nodes and 4 hops. Therefore, the jitter, which is smaller than the target value of 500 μs, is within tolerable range.


4 DATA ANALYSIS SOFTWARE In the case of a structure as a dynamic system, the measure of acceleration is the recording of dynamic response under random load, whereas the strain sensor records the static–dynamic strain. In an integrated monitoring system, the displacement quantity should be analyzed for dynamic bridge performance. However, directly determining displacement is difficult and incurs high costs because the variations in displacement signals are very weak. This study proposes a novel method to determine dynamic displacement by vibration acceleration signals. Through continuous monitoring, the system compiles a historical database for the structural dynamic responses of a bridge. The database can verify and modify bridge models, as well as accelerate the development of reliable diagnostic and prognostic tools for SHM. The data processing software in the system is based on VC++, and it is composed of a main processing procedure and a database. In the main procedure, three physical quantities can be selected for consideration: acceleration, dynamic displacement, and strain. 4.1 Analysis of acceleration signals First, for acceleration data, the inherent frequency of a structure can be determined by power spectral density (PSD) algorithm. Second, dynamic displacement data are similarly obtained with the use of numerical value integral to acceleration data. A new method that uses the numerical value integral to acceleration data in the time domain is proposed to obtain dynamic displacement data at a low cost. Although displacement data can be obtained after determining the integral value twice, numerical integration errors may be introduced, as the integral value is determined. A polynomial fitting method for extreme values is developed to eliminate the error. The method allows high-accuracy displacement data to be acquired easily. The sampling acceleration signal has a direct current component caused by the zero drift of sensors, so a(t) = a(t) + ε is denoted, where a(t) is the sampling acceleration, a (t) is the real acceleration, and ε is the DC component. After obtaining the integral value, the velocity signal and the displacement signal are determined as follows: t t v(t) = a(t)dt + v(0) = a (t) + (εt + δ) + v(0) 0

0

s(t) = 0

+

t

t

v(t)dt + s(0) =

where δ and η are the constant values formed as a result of a single and a double integrating ε, respectively. The use of trapezium formula yields v(i) ˆ = v(i ˆ − 1) +

[a (i) + a (i + 1)] + [ε(i/ fs ) + δ] 2 fs

+ v(0), ˆ (i = 1, 2 . . . n − 1)

(2)

1 [v(k) ˆ + v(k ˆ + 1)] ε(k/ fs )2 + sˆ (k) = sˆ (k − 1) + 2 fs 2 + (δ + v(0)) ˆ (k/ fs ) + η + sˆ (0), (k = 1, 2 . . . n − 2) Equation (2) shows that the velocity signal conˆ and the distains tread item ε(i/ fs ) + [δ + v(0)], placement signal contains tread item 12 ε(k/ fs )2 + [δ + v(0)](k/ ˆ fs ) + [η + sˆ (0)]. The polynomial fitting method for extreme values is then used to fit the tread items. With the time domain − 1) exist, integral, data points (ti , vi )(i = 0, 1, . . . , n k where t denotes the time. Note that fm(t) = m k=0 pkt ∈ is assumed, where Pk is the polynomial coefficient, and is a function class consisting of a polynomial expression with no more than m(m ≤ n − 1) of all n−1 n−1 [vi − fm(ti )]2 = i=0 [ m degrees. If I = i=0 k=0 vi − pktik]2 = min, according to the theory of conditional extremes on the functions of several variables, the following exists: m

n−1 ∂I j k =2 vi − pkti ti = 0, j = 0, 1, . . . , m ∂ pj (3) i=0 k=0 With the matrix expression, the formula is ⎡ ⎤ ⎤ n−1 n−1 n−1 m ti ti ⎥ vi ⎥ ⎢ n ⎤ ⎢ ⎢ i=0 ⎥⎡ ⎥ ⎢ i=0 i=0 ⎢ ⎥ p0 ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ n−1 n−1 n−1 ⎥ ⎢ n−1 2 m+1 ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ti ti ··· ti ⎥ ⎢ p1 ⎥ ⎢ ti vi ⎥ ⎢ ⎢ i=0 ⎥⎢ ⎥ ⎢ i=0 i=0 i=0 ⎥ ⎥⎢ . ⎥ = ⎢ ⎥ ⎢ ⎢ ⎥ ⎥ ⎢ . . ⎢ ⎥ .. .. ⎥ ⎢ . ⎥ ⎢ .. ⎥ ⎢ . ⎢ ⎥ ⎢ . . ··· . ⎥⎣ . ⎦ ⎢ ⎥ ⎥ ⎢ p ⎢ ⎥ ⎥ ⎢ n−1 m n−1 ⎢ n−1 ⎢ m n−1 m+1 2m ⎥ m ⎥ ⎣ ⎣ t t ··· t ⎦ ti vi ⎦ ⎡

i=0

i

i=0

i

i=0

i

i=0

The coefficient matrix of equations is confirmed to be a symmetric positive definite matrix; thus, a unique solution exists. In view of the tread items, the fitting polynomial is obtained: f1 (i/ fs ) = p1 (i/ fs ) + p0 , f2 (i/ fs ) = q2 (i/ fs )2

s (t) 0

201

+ q1 (i/ fs ) + q0

1 2 εt + δ + v(0)t + η + s(0) 2

(1)

(4)

where (i = 0, 1, . . . , n − 1). After the solution of the above-mentioned equations is derived, the error of the

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Begin

measuring points fitting curve

Install strain gauge

Stand stress loader exerts pressure Read the output value of reference measurement

Strain value (

)

Choose test model

Read the output value of strain sensor board

Curve fit using the least squares method

Fig. 8. Calibration process.

tread items can be eliminated. Chen et al. (2010) performed an in-depth examination of the algorithm, in which the mean square error can approach 0.04% in a numerical example.

4.2 Calibration of the strain sensor board As previously mentioned, the strain signal is fed into an ADC after data processing. However, quantizing the deformation of a foil strain gauge is difficult. Therefore, the strain sampling system should be calibrated before use. The calibration process (Figure 8) is described as follows. The system is calibrated with the use of a 15 × 15 × 15 C30 chip concrete model. A strain gauge is installed on the model and connected to both the strain sensor board and a conventional strain-measuring instrument. A compensation gauge is also attached to the model, which is perpendicular to the object. Then a standard stress loader exerts pressure on the chip concrete in the vertical direction. Under the same pressure, the output values of the wireless strain sensor board and the reference strain measurement are compared. Comparison of the real strain value with the AD sampling value reveals their mathematical relationship. Curve fitting is conducted with the use of least squares method, which yields y = 0.0615 × AD − 4.66, where y is the strain value, whose unit is με, and AD denotes the digital reading of ADC. Figure 9 is the fitting curve of the foil strain gauge. The fitting results show that the coefficient of determination is nearly 1, which is adequate for the requirement of accuracy. In our proposed system, every strain gauge is calibrated with the use of the method in Figure 8.

Digital reading of ADC

Fig. 9. Fitting curve of a foil strain gauge.

5 DEPLOYMENT OF THE SYSTEM This study investigated the Zhengdian Highway Bridge in Wuhan, China (Figure 10a), a prestressed, concrete structure, simply supported slab bridge. It is a series of simply supported spans without connection. The entire bridge is 484.36 m long, with 187.82 m of highway in the south–north connection line. The main bridge is 296.54 m long, which consists of 18 spans of 16 m each and two bridge abutments. Table 3 shows the detailed parameters of the bridge. A 3D finite element model for the bridge was developed to determine the best position for the sensor nodes.

5.1 Finite element analysis Integral modeling method with ANSYS software was used. The concrete structure is a SOLID65 unit, which is adopted to simulate 3D reinforced or nonreinforced concrete model. Eight nodes are defined. Each node has three degrees of freedom, that is, translation of the x, y, and z directions in the node coordinate system. For the unit of end bearing section, x, y two-way constraint is applied to simulate the fixed support and y-way constraint is to simulate the sliding support. Figures 10b and 10c show the integral analysis model. To analyze the strain of the key section under the most unfavorable load, a balanced and anunbalanced load (Figure 11) in bridge horizontal direction is used, the maximum strain value is then obtained. The bridge is a simply supported slab bridge that has a static set structure, so the force applied to it is determinate. The mid-span, quarter-span, and fulcrum sections are compared. The results of the finite element static analysis show that the maximum strain value of the monitored


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(b)

(a)

(c) Fig. 10. Test structure: Zhengdian Bridge (a) photograph, (b) axonometric drawing, and (c) front elevation.

Table 3 Fundamental parameters of the Zhengdian Bridge Designed load carrying capacity Bridge deck layout Structure

Longitudinal slope Superstructure

Material

Automobile: 20, trailer: 120 0.5 m crash barrier + 9.0 m roadway + 0.5 m crash barrier = 10.0 m Main bridge 4 + 0.02 + 18 × 16 + 0.02 + 4.5 = 296.54 m Northern connection 113.98 m Southern connection 73.48 m Northern 1.249%, southern 2.471%, all less than 4% Prestressed reinforced concrete hollow slab simply supported, nine slabs per span, slab height 0.8 m C50 precast concrete hollow slab

highway bridge is almost 101 με in the mid-span section. Modal frequencies and modal shapes are also calculated. For example, the first-order frequencies of the 7th, 8th, and 9th spans are 7.279, 7.280, and 7.278 Hz, respectively. 5.2 Instrumentation In the highway bridge structure model, the 7th, 8th, and 9th spans and the 10th, 11th, and 12th spans are symmetrically constructed. Therefore, for convenience in installation and in analysis of the results, the 7th, 8th, and 9th spans are selected for acceleration measurement, whereas the 10th, 11th, 12th, and 13th spans are selected for strain measurement. From the results of finite element static analysis, the side-span, mid-span, and quarter-span sections were set as monitoring sections,

and additional strain gauges were installed in places where the stress is relatively large, such as in the midspan section. Figure 12 shows the exact installation position of the strain nodes in the four spans, with triangles representing the positions of nodes. Accelerometers capture the dynamic response of the bridge under ambient and forced vibration, so they determine modal properties. To obtain a stronger signal, accelerometers should be installed at locations considered as the peak positions of order modes. Therefore, taking into account the mode shapes of the simple beam bridge, we placed six acceleration nodes on the mid-span and quarter-span sections of the 7th, 8th, and 9th spans. The results of finite element analysis on the bridge show that the first-order fundamental frequencies are below 10 Hz. The noise level is usually high in uncontrolled bridge environments, so oversampling is generally performed by reduction in relative noise energy to improve the signal-to-noise ratio. Therefore, a sampling rate of 100 Hz was chosen for this study. Oversampling reduces the effect of ADC quantization noise and is generally accepted to provide an additional bit of effective resolution for each power of four rate of oversampling. Each node run begins with building the network topology and synchronizing. After receiving a sampling command from the sink node, each node initiates the sampling. The 8 MB memory of the M25P80 serial flash in each node can be allocated to any combination of sensor channels on the node (two-dimensional accelerometer, temperature sensor, or strain sensors). The recorded data are then transmitted from each node to the base station by multi-hop communication. In this study, a complete cycle of sampling and data collection for the network produces 6 MB of data and takes

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(b)

(a)

Fig. 11. Unbalanced load and balanced load arrangement in the horizontal direction (unit: mm).

Slab Spacing

8.7m 7.11m 4.85m

0

0

7.43m 10th span

14.86m

11th span

13th span

12th span Longitudinal Distance

Fig. 12. Strain node position.

about an hour and a half. The total measurement is determined under a random load, that is, the bridge has random and nonspecific traffic. To determine the initialization value of the strain, the bridge was temporarily closed to traffic for 15 minutes before the measurements were taken. 6 ANALYSIS AND RESULTS The monitoring system aims to collect dynamic response data of the bridge when it is subjected to random and moving vehicle loads to help evaluate the state of the highway bridge during its life span. As an example of ambient vibration data, the vertical accelerations from the accelerometers at the quarterspan of the 7th, 8th, and 9th spans are shown in Figure 13. The sampling frequency was 100 Hz over 250 seconds and thus resulted in 25,000 samples per channel. Each figure includes plots of the signal and the power spectral density. The amplitudes of the ambient accelerations are about ±5 mg, but spikes of up to 20 mg are apparent. These spikes are presumably caused by heavy vehicles traveling on the highway. The PSD plots show clear and consistent peaks at frequencies at several nodes. These spectral peaks are distinct in lower frequencies, which correspond to the vertical vibration mode shapes of the bridge, as will be shown later. The PSD plots indicate that the low-frequency noise level is very small compared with the peaks of the spectra. In this study, mode shapes were derived from six vertical acceleration measurements with the use of multi-

variate autoregressive models (ARX) method to reveal the first three order modes. In the multivariate ARX system model, stochastic system input is unmeasured, which can be reasonably assumed to have the characteristics of white noise. Figure 14 shows several estimated vertical modes with frequencies in the 0 Hz–20 Hz frequency band. These mode shapes correlate in terms of frequency and shape with finite element analysis from a model constructed from as-built drawings. As the number of data points increases, the model shapes would match better. From the above-mentioned integral numerical value of acceleration data, the measured velocity and dynamic displacements curves with the elimination of tread items errors (Figure 15) under the random load were obtained. The dynamic displacements in Figure 15 remain nearly within the baseline. Figure 15 also shows that the measured maximum value is less than 10 mm in 40 seconds, which is smaller than 27 mm, the designed maximum permissible value under standard calculation load. For in-service bridges, a reasonable impact coefficient response reflects the safety performance state of bridges to a certain degree. From the displacement, the coefficient of impact μ of the bridge can be calculated as follows: 1+μ=

(d + a ) d

(5)

where d is the acceleration integral value under the worst designed loads, and a represents the maximum displacement in the displacement diagram. Table 4 shows the impact coefficients of the 7th, 8th, and 9th


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Fig. 13. Acceleration time history and power spectral density for a vertical sensor at (a) 7th quarter-span, (b) 8th quarter-span, and (c) 9th quarter-span.

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Fig. 14. Mode shape estimates derived from application of ARX.

spans, in which the measured coefficients are all lower than the designed value. The statistic dynamic strain under random live load could be obtained with the data gathered from the strain sensors. Examination of the strain data shows that the measured maximum strains of the 10th, 11th, and 12th spans are 22, 12, and 14 με under public traffic, respectively. Comparing the measured strain data with the designed bound value maintains the strain coefficient of checkout. The coefficients of checkout of the bridge

spans are between 0.10 and 0.34, and they indicate that the safe operating limits of the highway bridge are relatively sufficient. Overall, our study shows that the integral continuous monitoring system represents a simple, low-cost method to obtain long-time, statistic dynamic response data for the assessment of bridge health condition. The data gathered can help verify a design method or indicate the level of service life remaining in the structure.


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Fig. 15. Velocity and dynamic displacement time history in the mid-span section of (a) the 7th span, (b) the 8th span, and (c) the 9th span.

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Table 4 Impact coefficient of the bridge structure (1 + μ) Impact coefficient (1+μ) Span number 7 8 9

Node position

Measured value

Calculated value

Quarter-span Mid-span Quarter-span Mid-span Quarter-span Mid-span

1.028 1.030 1.030 1.037 1.080 1.050

1.3350 1.3351 1.3350

7 CONCLUSIONS We have developed an integrated bridge health monitoring system based on a WSN for bridge SHM. The system is comprehensive and practical because it combines a bottom data collection platform with upper data analysis software. The platform consumes ultralow power and is flexible, indicating scalability to all kinds of sensors, including acceleration, temperature, and strain sensors. The network topology uses multihops, with up to four hops in our experiment. The software enables the analysis of multiple types of dynamic response parameters, including vibration acceleration, dynamic displacement, and dynamic strain for continuous bridge health monitoring. The system was deployed in the Zhengdian Highway Bridge. The ability of the proposed system to capture data, transmit data via multi-hop wireless communication, and analyze monitoring data has been validated through tests performed on the bridge for continuous SHM of in-service bridges. In contrast to the finite element analysis and theoretical analysis results, those for the proposed system coincide with one another, certifying the feasibility and validity of the system. The limitations of the current work present opportunities for future research on bridge SHM systems based on WSNs. The time consumed for network topological discovery is up to nearly 1 minute in the four-hop network. This result may be attributed to the existence of field noise interferences, which are unstable. Extending the number of hops will prolong the time spent on topological discovery. In addition, packet loss was observed during the tests. The final packet delivery ratio is nearly 99.96%. A good solution to this problem is to incorporate a data recovery mechanism into sink nodes. To satisfy the requirements of bridge SHM, more strain sensors should be considered, including vibrating steel wire transducers, which are as widely used as resistance strain gauges.

ACKNOWLEDGMENTS The authors give special thanks to the staff and management of the Zhengdian Bridge Management Corporation for their close cooperation with them in every step of the project. They also give thanks to graduate students, Yin An, Liu Zhuo, Tang Xuanlai, and Chen Weizhen. This work was funded by the National Natural Science Foundation of China (No. 60802002) and Special Fund from the Central Collegiate Basic Scientific Research Bursary (No. 2011TS140). The authors would like to thank editors and anonymous reviewers for their insightful comments. REFERENCES Adeli, H. & Jiang, X. (2006), Dynamic fuzzy wavelet neural network model for structural system identification, Journal of Structural Engineering, ASCE, 132(1), 102–11. Akyidiz, I. F., Su, W., Sankarasubramaniam, Y. & Cayirci, E. (2002), Wireless sensor network: a survey, Computer Networks, 38(4), 393–422. Bocca, M., Cosar, E. I., Salminen, J. & Eriksson, L. M. (2009), A reconfigurable wireless sensor network for structural health monitoring, in Proceedings of the 4th International Conference on Structural Health Monitoring on Intelligent Infrastructure (SHMII-4) 2009, Zurich, Switzerland, 22–24. Bocca, M., Eriksson, L. M., Mahmood, A., Jantti, R. & Kullaa, J. (2011b), A synchronized wireless sensor network for experimental modal analysis in structural health monitoring, Computer-Aided Civil and Infrastructure Engineering, 26(7), 483–99. ´ J. & Koivo Bocca M., Toivola J., Eriksson L. M., Hollmen H. (2011a), Structural health monitoring in wireless sensor networks by the embedded Goertzel algorithm, in 2011 IEEE/ACM International Conference on Cyber-Physical Systems (ICCPS), Chicago, United States, 206–14. Caddemi, S. & Morassi, A. (2007), Crack detection in elastic beams by static measurements, International Journal of Solids and Structures, 44(16), 5301–15. Carden, E. P. & Brownjohn, J. M. W. (2008), Fuzzy clustering of stability diagrams for vibration-based structural health monitoring, Computer-Aided Civil and Infrastructure Engineering, 23(5), 360–72. Ceriotti, M., Mottola, L., Picco, G. P., Murphy, A. L., Guna, S., Corra, M., Pozzi, M., Zonta, D. & Zanon, P. (2009), Monitoring heritage buildings with wireless sensor networks: the Torre Aquila deployment, in Proceedings of the 2009 International Conference on Information Processing in Sensor Networks, San Francisco, United States, 277–88. Chen, W. Z., Wang, B. W. & Hu, X. Y. (2010), Acceleration signal processing by aumerical integration, Journal of Huazhong University of Science and Technology (Nature Science Edition), 38(1), 1–4. Gangone, M. V., Whelan, M. J. & Janoyan, K. D. (2011), Wireless monitoring of a multispan bridge superstructure for diagnostic load testing and system identification, Computer-Aided Civil and Infrastructure Engineering, 26(7), 560–79. Garc´ıa, O., Vehi, J., Matos, J. C., Henriques, A. A., & Casas, J. R. (2008), Structural assessment under uncertain


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