Structural health monitoring syste m of a cable...

Structural health monitoring system of a cable-stayed bridge using a dense array of scalable smart sensor network

Soojin Choa, Shin Ae Jangb, Hongki Job, Kirill Mechitovc, Jennifer A. Riced, Hyung-Jo Jung*a,

Chung-Bang Yuna, Billie F. Spencer Jr.b, Tomonori Nagayamae, and Juwon Seof

aDept. of Civil and Environmental Engrg., KAIST, Daejeon 305-701, Korea;

bDept. of Civil and Environmental Engrg., Univ. of Illinois, Urbana, IL 61801, USA; cDept. of Computer Science., Univ. of Illinois, Urbana, IL 61801, USA;

dDept. of Civil and Environmental Engrg., Texas Tech Univ., Lubbock, TX 79409, USA; eDept. of Civil Engrg., Univ. of Tokyo, Tokyo 113-8656, Japan;

aLong Span Bridge Research Team, Hyundai Inst. Const. Tech., Yongin 449-716, Korea;

ABSTRACT

This paper presents a structural health monitoring (SHM) system using a dense array of scalable smart wireless sensor network on a cable-stayed bridge (Jindo Bridge) in Korea. The hardware and software for the SHM system and its components are developed for low-cost, efficient, and autonomous monitoring of the bridge. 70 sensors and two base station computers have been deployed to monitor the bridge using an autonomous SHM application with consideration of harsh outdoor surroundings. The performance of the system has been evaluated in terms of hardware durability, software reliability, and power consumption. 3-D modal properties were extracted from the measured 3-axis vibration data using output-only modal identification methods. Tension forces of 4 different lengths of stay-cables were derived from the ambient vibration data on the cables. For the integrity assessment of the structure, multi-scale subspace system identification method is now under development using a neural network technique based on the local mode shapes and the cable tensions.

Keywords: Smart sensor, wireless sensor network, structural health monitoring, cable-stayed bridge, test-bed

1. INTRODUCTION Bridges are valuable national assets which ensure economic prosperity and public safety. However, many of them in modern countries have reached their design life so that they need to be retrofitted, or even reconstructed, to be remaining in service. In the United States, more than 149,000 bridges – almost quarter of total number (603,000) of bridge - are estimated to be structurally deficient or functionally obsolete [1]. Thus, the ability to assess the structural condition and possibly to increase the service life has been pursued widely by many researchers and engineers. By the efforts, many bridges get to have their own structural health monitoring (SHM) system to assess the structural conditions and defeats. However, the cost of SHM on large structures is still high, especially due to cabling work for constructing tethered centralized systems, which limits the number of sensors to be installed and wider implementation to the other structures. For example, it has been reported that the Bill Emerson Memorial Bridge is instrumented with 84 wired accelerometers with an average cost of over $15K per channel [2], and Tsing Ma Bridge with more than 600 sensors with an average cost of over $27K per channel [3]. If expensive cost of data acquisition systems, such as corresponding signal conditioner, analog filter, and amplifier for each type of sensor, and maintenance cost are additionally considered, then the total cost of a SHM system becomes higher.

Wireless smart sensor technology is getting attention as an economic alternative of long-term, scalable SHM of civil infrastructure. With its wireless communication and onboard computing capability, it provides flexible deployment of sensors, easy installation without tethering work, decentralized signal conditioning, and efficient data management strategy at a lower cost than traditional wired monitoring systems. There are many wireless smart sensor platforms

Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2010, edited by Masayoshi Tomizuka, Chung-Bang Yun, Victor Giurgiutiu, Jerome P. Lynch, Proc. of SPIE

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developed in academia and industry at a low cost less than $1000 USD per unit with more than one sensing channel [4, 5].

Several researchers have employed wireless smart sensors to monitor bridge structures [4, 5, 6, 7, 8], providing important insight into the opportunities and challenges for wireless smart sensor network (WSSN) technology for long-term monitoring. Critical issues identified include: (1) data synchronization and recovery of missing data, (2) on-board and decentralized processing, (3) autonomous operation, (4) power management and energy harvesting, and (5) environmental hardening. To address these research challenges, an international test bed employing a cable-stayed bridge, the second Jindo Bridge, in South Korea was developed using a state-of-the-art wireless smart sensor technology through a trilateral research collaboration among Korea (KAIST), the US (University of Illinois at Urbana-Champaign), and Japan (University of Tokyo). The hardware and software of wireless smart sensors are developed to overcome the issues while keeping low cost. A multi-metric sensor board compatible with Imote2 [9] is developed for vibration-based monitoring of large structures, and it is modified to be interfaced with an anemometer for wind environment investigation around the structures. Software for the wireless smart sensors is developed in service-oriented architecture (SOA), which has been proposed as a way to use this design philosophy in building dynamic, heterogeneous distributed applications [10, 11]. Operating software for base stations are also developed for autonomous and effective monitoring of large structures. After serious consideration of environmental hardening, a total of 70 wireless smart sensors and two base stations have been deployed to monitor the bridge. Measurement has been carried out during 4 months, and the performance of the system has been evaluated in terms of hardware durability, software stability, power management, and energy harvesting options. Measured acceleration data is analyzed in the frequency domain to be used for monitoring of the bridge. For deck and pylons, output-only modal identification are carried out using two output-only modal identification method, FDD and SSI, and for cables, tension forces are estimated by a vibration method. Based on the results, discussions are made on the monitoring strategy utilizing the WSSN for comprehensive SHM of the cable-stayed bridge.

2. BRIDGE SHM SYSTEM USING WIRELESS SMART SENSORS Figure 1 shows the concept of a bridge SHM system constructed using WSSN. For effective communication, wireless smart sensor nodes can be clustered according to their positions, and hierarchy is formed in the cluster. A cluster is governed by a head node, which has more powerful functionality than the others (leaf nodes) in the cluster. The head node gets commands from the gateway node, interacts with leaf nodes in the cluster, and sends data to the gateway node. A gateway node, which is different from a wireless smart sensor node due to its lack of sensing capability, is connected to the base station and communicates with head nodes directly. If there is no head node, then it communicates with all leaf nodes directly. In this section, the hardware and software of bridge SHM system using WSSN will be described.

Head node

Leaf nodes

Base Station

Gateway node

Cluster n

Cluster n+1Cluster n+2

Cluster n‐1

1. When head nodes exist, gateway node communicates with head nodes, not leaf nodes.2. When head nodes do not exist, gateway node communicates with leaf nodes directly.

Figure 1. Concept of bridge SHM system using wireless smart sensor networks



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2.1 Hardware of wireless smart sensors

The key hardware components of a wireless smart sensor node are an Imote2 [9], a sensor board, and a battery board. The Imote2 is one of high-performance wireless computing modules, having an Intel’s PXA271 XScale® processor running at 13-416 MHz and an MMX DSP coprocessor. It contains sufficient memory capacity of 256kB SRAM, 32MB FLASH, and 32MB SDRAM, which enables long-time measurement as well as on-board manipulation of the measured data. It has an onboard antenna for 2.4GHz wireless communication, and an alternative 2.4GHz antenna can be equipped additionally for field applications which require longer wireless communication.

An Imote2 can be interfaced with a sensor board, either an SHM-A (structural health monitoring-acceleration) board to measure multi-metric data or an SHM-W (structural health monitoring-wind) sensor board to measure wind speed and direction by interfacing with a 3-axis anemometer. The sensor boards have been designed to monitor civil infrastructure through the Illinois SHM Project [12], an interdisciplinary collaborative effort by the researchers in civil engineering and computer science at the University of Illinois at Urbana-Champaign. As shown in Figure 2(b), the SHM-A board has a 3-axis accelerometer (ST Microelectronic’s LIS344ALH), and the analog acceleration signals from the accelerometer are digitized by the embedded Quickfilter QF4A512, which has a 4-channel, 16-bit analog to digital converter (ADC) and programmable signal conditioner with user-selectable sampling rates and programmable digital filters. The SHM-A board also contains temperature, humidity, and light sensors. This board also provides extra external input connector to measure data from various types of sensors such as anemometers and strain gages. Four sampling frequencies (10, 25, 50, 100 Hz) have been pre-programmed on the SHM-A board for bridge monitoring applications, and the sampling rate can be modified by designing appropriate filters for the QF4A512. To monitor the wind environment, which is one of the most critical factors which affect the bridge responses and condition in Korea, a SHM-W board has also been developed by modifying the SHM-A board to have three external 0–5V input channels to be interfaced with a 3-D ultrasonic anemometer as shown in Figure 2(c). The RM Young Model 81000 3-D ultra-sonic anemometer is selected due to its high resolution (wind speed: 0.01m/s, wind direction: 0.1 degree), good accuracy (wind speed: ±1%, wind direction: ±2 degrees), and the long-term durability against harsh outdoor environment. The wind speed in horizontal and vertical directions is recorded after the precise synchronization with the bridge acceleration data obtained from wireless smart sensors adapting the SHM-A boards.

SHM-A Board (rev. 4.0)

External Antenna(Antenova 2.4GHz)

Imote2

Battery Board(IBB2400CA)w/ 3 AAA Batteries

(a) Wireless smart sensor (b) Components of SHM-A Board

(c) SHM-W Board and Anemometer (RM Young Model 81000)

(d) Solar panel (SPE-350-6) and lithium-polymer rechargeable battery for energy harvesting

Figure 2. Hardware components of wireless smart sensor



The IBB2400CA battery board powers the Imote2 using a series of three 1.5V AAA batteries. It contains a power management integrated circuit (PMIC) which enables powering the node using energy harvesting devices. In this study, solar panels with rechargeable batteries have been adapted on several wireless smart sensor nodes to investigate the feasibility of sustainable energy harvesting. The Solarworld SPE-350-6 solar panel (9V-350mA) was employed to the battery board with the Powerizer lithium-polymer rechargeable battery, of which voltage can be fully charged up to 4.2V with 10,000-mAh capacity.

For the field deployment, some adjustments have been made to the wireless smart sensor nodes. An Antenova gigaNova Titanis 2.4 GHz external dipole antenna has been additionally equipped to secure longer wireless communication. The battery boards with AAA batteries have been modified to employ a series of three 1.5V D-cell batteries with large capacities (21000mAh) for longer operation without changing batteries. All the components of a wireless smart sensor is stacked as shown in Figure 2(a) and contained in a hardened plastic enclosure to be protected from harsh outdoor environment.

2.2 Software for wireless smart sensors

An open-source middleware services toolsuite, which interacts between the target SHM applications and operating software of wireless smart sensors, was developed by ISHMP to help the civil engineers easily code into the wireless smart sensor nodes [12]. The toolsuite, called ISHMP Services Toolsuite, contains basic middleware to provide high-quality sensor data and to transfer the data reliably to the base station via wireless communication as well as a library of numerical algorithms.

The toolsuite components are categorized into foundation services, tools and utilities, application services, and continuous and autonomous monitoring services. The foundation services provide the fundamental functionalities to measure synchronized sensor data with high confidence [13, 14]. The application services are the numerical algorithms to implement SHM applications on the Imote2, including modal identification and damage detection algorithms. The tools and utilities support network maintenance and debugging. This category has essential services for full-scale monitoring as well as sensor maintenance. Several key services will be explained here: RemoteSensing is the application for the remote data measurement; DecentralizedDataAggretation is the application for remote data measurement and subsequent decentralized on-board computation of the correlation functions of measured data in localized clusters; imote2comm is a terminal to interact with a gateway node; RemoteVbat is an application for checking the battery level of remote sensors; and TestRadio is the tool for assessing radio communication quality.

2.3 Base station

The base station controls the network by (1) sending messages to the leaf nodes, (2) storing the transmitted data from the WSSN, (3) processing received data, and (4) transferring the data to the remote server via internet. In this study, a base station is composed of an industrial personal computer (PC), an uninterrupted power supply (UPS) backup, a gateway node, and ADSL internet modem contained in an environmentally hardened enclosure for outdoor application as shown in Figure 6. The gateway node consists of an Imote2 stacked on an IIB2400 interface board [9] connected to the PC via a USB/UART port without a sensor board.

2.4 Operating software of wireless smart sensor network

To operate WSSN with large number of wireless smart sensor nodes, 3 problems are seriously considered: (1) energy saving software architecture, (2) data inundation, and (3) continuous and autonomous operation. To save batteries of large number of nodes, SnoozeAlarm, which allows the network to sleep default and wakes up periodically for a short time to listen to broadcasted commands, is embedded into the wireless smart sensor nodes. To prevent data inundation from large number of sensor nodes, ThresholdSentry, which wakes up sentry nodes at a preset interval to measure a short period of data, is applied to the network along with selection of several sentry nodes. If the measured data of sentry nodes exceeds a preset threshold which may indicate a severe event to the bridge, the sentry nodes send alarm to the gateway node, and the base station subsequently wakes up the entire network for data measurement. AutoMonitor, an autonomous SHM network management application which combines RemoteSensing, SnoozeAlarm, and ThresholdSentry, is installed on the PC of the base station to enable the automatic, continuous monitoring with reduced power consumption. For remote operation of WSSN via internet, VNC (Virtual Network Computing) server and FTP (File Transfer Protocol) server are additionally installed on the PC to control the PC remotely and to download the measured data remotely, respectively.



3. DEPLOYMENT OF BRIDGE SHM SYSTEM ON SECOND JINDO BRIDGE 3.1 Bridge Description

The Jindo Bridges are twin cable-stayed bridges connecting Haenam (a city on the mainland of Korea) with the Jindo Island as shown in Figure 3. Each of these bridges consists of a 344-m main span and two 70-m lateral spans. The 2nd Jindo Bridge has a streamlined steel box girder supported by 60 parallel wire strand (PWS) cables. The 2nd Jindo Bridge is selected as the test bed under the permission of unfettered access by the authority because of (i) the existing SHM system to provide the comparable data with measured one by WSSN and (ii) the complete design and construction documents for full interpretation of the bridge.

Figure 3. Jindo Bridges (left: 2nd Jindo Bridge, right: 1st Jindo Bridge)

3.2 Sensor deployment strategy

The developed hardware and software framework described in Section 2 has been deployed on the 2nd Jindo Bridge to realize a large-scale and autonomous SHM system using WSSN. The network topology was carefully determined to ensure the reliable communication on the bridge. Since the communication range of Imote2 with an external antenna is about 200m shorter than the total length of the bridge, the network was divided into two sub-networks with different radio channels: one on the Jindo side and the other on the Haenam side as in Figure 4. A total of 70 leaf nodes were deployed on the bridge. They contain SHM-A sensor boards to measure 3-axis acceleration mainly, except one node containing a SHM-W board interfaced with an anemometer. The Jindo sub-network consists of 33 nodes with 22 nodes on the deck, 3 nodes on the pylon, and 8 nodes on the cables. The Haenam sub-network consists of 37 nodes with 26 nodes on the deck, 3 nodes on the pylon, and 7 nodes on the cables.

Figures 4-5 show the locations of 70 leaf nodes and photos of various types of nodes. They were enclosed in water-tight plastic enclosures for protection from moisture and dust of harsh outdoor environment. The deck/pylon nodes were mounted using one-directional magnets attached on the bottom of enclosures, and the cable nodes were mounted on aluminum plates with round interface to fit the round cables. An anemometer was installed on a 5m-tall steel bar at the center of deck to prevent any interruption of the bridge on the wind measurement, while the leaf node with SHM-W board incorporating the anemometer was installed underneath of the deck to secure the line-of-sight to the base station.

3.3 Details of established wireless smart sensor network

Each sub-network is controlled by a corresponding base station located at the tops of the concrete piers supporting the steel pylons of the 1st Jindo Bridge, which is parallel to the 2nd Jindo Bridge. The locations of base stations are selected to secure consistent line-of-sight communication with the leaf nodes.

The parameters of WSSN for SHM of the 2nd Jindo Bridge have been set as Table 1 after a series of laboratory and preliminary in-field tests. For ThresholdSentry application, two types of sentry nodes are selected: vibration-sentry nodes for acceleration measurement and a wind-sentry node for wind measurement. The combination of two types of sentry nodes provides efficient measurement strategy during the events causing large responses of the bridge by wind and traffics. For both types of sentry nodes, two types of threshold values were preset: for normal condition and for extreme condition, such as typhoon and earthquake. One time of measurement in a day is set to be carried out for exceeding threshold values of normal condition, while unfettered measurement is set for exceeding threshold values of extreme condition as shown in Table 1.



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(a) Node under deck (b) Node on pylon top

(w/ solar panel) (c) Node on cable (w/ solar panel)

(d) Anemometer at center of deck

(e) Reference sensors at center of the deck

Figure 5. Various types of wireless smart sensor nodes installed on the 2nd Jindo Bridge

Figure 6. Base station of Jindo-

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Table 1. Network parameters

Parameter ValueNo. of sampled data 5,000Sampling rate 10 HzAnti-aliasing filter 4 HzNo. of Sensing events 1 per daySnoozeAlarm sleep time 750 msSnoozeAlarm listen time 15 sThreshold sensing interval 10 minThreshold sensing time 10 secThreshold for vibration-sentry 10 mg (normal condition)

50 mg (extreme condition) Threshold for wind-sentry 3 m/sec (normal condition)

8 m/sec (extreme condition)



4. EVALUATION OF THE SHM SYSTEM 4.1 Battery power consumption

The battery voltage levels of leaf nodes have been monitored during 2 month using RemoteVbat command, provided by ISHMP services toolsuite, and they are shown in Figure 7. The average voltage with three brand-new batteries on each node was about 4.6V. From 27 August to 8 September, a series of measurements were carried out to optimize network performance, which results in the rapid drop of battery power as shown in Figure 7(a). After 8 September, the AutoMonitor application has controlled the network and the power consumption has become approximately linear. The minimum onboard voltage required for sensing is about 3.6V so that it can be concluded that a series of three D-cell batteries are able to last about 2 months of monitoring.

4.2 Energy harvesting using solar panel

The voltage levels of 8 nodes powered by solar panels and rechargeable batteries (5 on cables, 2 on pylon tops, and 1 under the deck) are also investigated to check the feasibility of solar-based energy harvesting. Figure 7(b) shows the voltage levels of the rechargeable batteries of 6 nodes (for cables and deck) powered by solar panels during 1.5 months of monitoring. It is shown that the voltage levels have stayed around 4.15V, except one node under the deck, whose solar panel faces intentionally downward without being exposed to direct sunlight.

4.3 Measured acceleration and wind data

Figure 8 shows examples of the ambient acceleration data measured on the deck, pylon, and cable in 3 directions. The amplitudes of the acceleration due to the passing traffic on the deck are found to be large enough for mode extraction, especially for vertical modes (z-axis). Similar to the deck, the in-plane (perpendicular to the cable in the vertical plane) vibration of the cable is much larger than the other components. The amplitudes are also found to be sufficiently large for mode extraction, which will be used for estimation of the cable tension forces.

(a) Average on-board voltage (b) Battery status of rechargeable batteries with solar panels

Figure 7. Evaluation of battery and sustainable energy harvesting

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(a) On the deck of main span (b) At the top of a pylon (c) On a cable

Figure 8. Examples of measured acceleration (Jindo sub-network)



The power spectral densities (PSD) of the vibration data have been investigated. Figure 9 shows PSDs of the deck accelerations obtained from a wireless node and from the existing wired monitoring system. Though the data from wired monitoring system was measured in 2007 while the wireless sensor data was measured in 2009, the peak frequencies of two PSDs implying modal frequencies look very similar at around 0.44, 0.66, 1.05, 1.37Hz and so on.

The wind speed and direction has been successfully measured using the 3D ultra-sonic anemometer at the mid span. The data are synchronized with vibration data measured by SHM-A sensor board. Figure 10 shows an example of measured wind speed and direction. In the example data, the wind speed was 4-6 m/sec and the direction is between -10 to 20 degree to the longitudinal direction of the bridge.

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5. DATA ANALYSES 5.1 Output-only modal identification

Modal properties such as natural frequencies, mode shapes, and modal damping ratios, play key roles for SHM of bridges. To analyze the ambient (or operational) acceleration data excited by ambient sources, such as wind and traffic, two output-only modal identification methods are employed in this study: frequency domain decomposition (FDD) [15] and stochastic subspace identification (SSI) [16] methods.

Modal analyses are carried out on two sets of data obtained from Haenam- and Jindo- side WSSNs using both FDD and SSI. Because the WSSNs are not synchronized to each other during the measurement, the data from each WSSN were analyzed independently, and then combined subsequently. Table 2 gives the details of the identified modal properties from independent WSSN, including the natural frequencies from two WSSNs. The results from different modal identification methods are found to be consistent to each other. Several modes (DL1, DV2, DT1, and PB1) are found undetected by FDD. Though longer acceleration records may result in modal properties in better fidelity extracted by both FDD and SSI, longer data collection will drastically increase the time for transmitting data packets for a large-scale WSSN. The identified natural frequencies by both methods are compared with those obtained from the existing wired SHM system and from the FE analysis. The identified natural frequencies show excellent agreements with the frequencies obtained from the wired monitoring system in 2007. The results are also found to be very close to those from the FE analysis up to the 3rd vertical modes, while those for the higher modes are generally larger than the FE results. However, the differences are found to be within 16%.

The modal properties from each WSSN are combined to provide the global information for SHM. Four overlapped reference nodes at the mid span shown in Figure 5(e) are employed to construct the global mode shapes using the least-square method. Examples of the combined mode shapes are compared with those from the FE analysis in Figure 11. They show excellent agreements to each other with MAC values of 0.943-0.986, which reinforces the performance of the current WSSNs. The accuracy of the combined modal information may be improved by a decentralized approach to estimate global modal information, which is developed by [17]. Also, software is currently under development for synchronization of two separated base stations and expected to be implemented on the 2nd Jindo Bridge



Table 2 Natural frequencies extracted by SSI and FDD (Haenam-side on 9/8/2009, Jindo-side on 9/11/2009)

(a) DV1 (MAC: 0.957)

(b) DV2 (MAC: 0.986)

(c) DV3 (MAC: 0.975)

(d) DV6 (MAC: 0.949)

(e) DV9 (MAC: 0.943)

Figure 11. Identified combined mode shapes (left) and those from FE model analysis (right)

5.2 Estimation of cable tension forces

The 2nd Jindo Bridge has a total of 60 stay cables of parallel wire strand (PWS). The bridge is symmetric along the longitudinal as well as the lateral directions. Each pylon holds 30 cables; 15 cables on each of east and west sides. The cables are categorized into 4 groups with different cross sections (i.e., 7 139φ × , 7 109φ × , 7 73φ × , and 7 151φ × ) as shown in Figure 12. The above designations indicate the number of steel wires of a 7mm in diameter. High-damping rubber dampers are installed on cable anchors to reduce the wind-induced vibration of the cables.

No. Modes Mode Shape SSI (Hz) FDD (Hz) Wired monitoring system (Hz)

FE analysis(Hz) Haenam-

side Jindo-side

Haenam-side

Jindo-side

1 DL1 1st longitudinal 0.2998 0.2985 - - - 0.31372 DV1 1st vertical 0.4347 0.4380 0.4492 0.4492 0.4395 0.44223 DV2 2nd vertical 0.6619 0.6439 - 0.6445 0.6592 0.64714 DV3 3rd vertical 1.0371 1.0364 1.0352 1.0352 1.0498 1.00105 DV4 4th vertical 1.3481 1.3555 1.3379 1.3379 1.3672 1.24726 DV5 5th vertical 1.5755 1.5759 1.5723 1.5723 1.5869 1.34907 DV6 6th vertical 1.6618 1.6660 1.6602 1.6699 1.6602 1.45968 DT1 1st torsional 1.8278 1.8410 - - - 1.78889 DV7 7th vertical 1.8844 1.8860 1.8848 1.8848 1.8555 1.585810 DV8 8th vertical 2.2712 2.2731 2.2656 2.2754 2.3193 2.115411 PB1 1st bending 2.4107 2.3890 2.4121 - 2.3682 2.139212 DV9 9th vertical 2.8127 2.8266 2.8027 2.8320 2.8076 2.5612



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bsLs 1161bsLs 1161

Among the cables, 10 east-side cables with larger tension forces are selected to estimate the tension forces as shown in Figure 12. Table 4 shows the general properties of the cables. The effective lengths of the cables are obtained from the work by [18]. It is worthy to note that the leaf nodes monitoring the cables are installed at about 3m height from the onset of the cables connected to the deck, which is available for regular battery change while the rubber dampers does not affect the response much.

Figure 12. Arrangement of stay cables and wireless sensors on cables (sensor numbers in parentheses)

Table 3. Properties of the cables monitored

Table 4. Comparison of estimated tension forces with those from previous regular inspections in 2007 and 2008

*The differences from regular inspection in 2008 are shown in the parentheses.

Figure 8(c) shows an example of the 3-axis acceleration data measured on a cable of Jindo-side span. The frequency-domain analyses were carried out to obtain Fourier amplitude spectra (FAS) for the cable motions along with the FAS for deck motions, and it was found out that many peaks are apparent in the FAS for the vertical cable vibration, while some of them are from the deck motion owing to the interaction between the deck and cables particularly in the vertical direction. Since the cables of the 2nd Jindo Bridge are slender with small sag, the modal properties of the cable are very similar in the vertical and lateral directions. Taking advantage of this characteristics, the natural frequencies of cables, not frequencies caused by the cable-deck interaction, are extracted. For example, five natural frequencies are identified

Cables HC4, JC4 HC6, JC6 HC9, JC9 HC13, JC13 HC15, JC13Cable type 7 151φ × 7 151φ × 7 73φ × 7 109φ × 7 139φ ×Elasticity (tonf/mm2) 20.0 20.0 20.0 20.0 20.0 Area (mm2) 5811.0 5811.0 2809.0 4195.0 5349.0Length (m) 97.10 65.00 83.17 141.76 174.15Effective length (m) 95.38 63.33 79.01 136.87 169.69Unit mass (ton/m) 0.00486 0.0486 0.00236 0.00354 0.00448Design cable sag (mm) 256.0 96.0 221.0 537.0 809.0Design tension force (tonf) 237.0 271.0 90.0 160.0 202.0Allowable tension force (tonf) 470.0 470.0 227.0 339.0 433.0

Cables (East-side) Estimated tension forces (tonf) Initial design values (tonf)

Maintenance thresholds (tonf) WSSNs

in 2009 Previous inspectionsin 2007 in 2008

Haenam-side

HC4 274.0 (2.04)* 262.7 268.4 246.2 329 HC6 294.7 (-3.19)* 304.6 304.1 271.8 329 HC9 89.3 (0.90)* 86.9 88.5 87.6 158 HC13 170.2 (3.00)* 164.0 165.1 163.6 237 HC15 224.9 (2.18)* 219.9 220.0 204.8 303

Jindo- side

JC4 254.0 (1.30)* 245.1 250.7 245.9 329 JC6 274.5 (-1.09)* 282.0 277.5 271.5 329 JC9 88.5 (2.15)* 85.5 86.6 88.2 158 JC13 154.3 (2.33)* 148.3 150.7 164.1 237 JC15 216.8 (0.14)* 214.1 216.5 201.3 303

Proc. of SPIE Vol. 7647 764707-10


for two cables: 0.645, 1.294, 1.948, 2.598, and 3.247Hz for Cable JC15 with Node C-JE8, and 0.772, 1.514, 2.275, 3.027, and 3.789Hz for Cable JC13 with Node C-JE7. The natural frequencies are found to be almost proportional to the order of modes ( n ), which is also a dynamic characteristic of a slender cable with little bending and sag effect [19, 20].

Based on the identified dominant frequencies, the tension forces of 10 cables are estimated as Table 5. The estimated tension forces are compared with those obtained from two previous regular inspections in 2007 and 2008, those from the initial design, and those from the maintenance thresholds which are 60% of allowable tension forces of the cables [21]. The current estimations are found to be very close to the tension forces from two previous inspections with less than 4% difference. The tension forces of 8 cables have increased slightly with time, while those of 2 cables (HC6 and JC6) supporting the side spans have slightly decreased. The estimated cable tension forces are generally larger than the initial design values (10% at maximum) except JC13. All cable tension values are well within the maintenance thresholds, indicating that the cables are in safe operation.

6. CONCLUDING REMARKS This paper reports on the collaborative research on a state-of-the-art wireless smart sensor technology among Korea (KAIST), the US (University of Illinois at Urbana-Champaign), and Japan (University of Tokyo). A structural health monitoring system using 70 wireless smart sensors and 2 base stations has been successfully deployed on a cable-stayed bridge, the 2nd Jindo Bridge, in South Korea to verify the performance of the system along with its components. The result of the deployment can be summarized as below:

1) Hardware and software of wireless smart sensor node are developed for low-cost and efficient monitoring. SHM-A and SHM-W sensor boards were developed to be interfaced with Imote2 to measure 3-axis acceleration and wind. Basic software is also developed in service oriented architecture.

2) Operating software for base stations are also developed for autonomous and effective monitoring of large structures in consideration of energy saving software architecture (SnoozeAlarm), data inundation (ThresholdSentry), and continuous and autonomous operation (AutoMonitor).

3) After the deployment, it is shown that the wireless smart sensor network can be operated for 2 month without battery change with the help of autonomous and efficient monitoring strategy, and that the solar panels incorporating with rechargeable batteries provides sustainable energy during the monitoring period, which shows the feasibility of solar-based energy harvesting for wireless smart sensor network.

4) Modal properties of the bridge were successfully obtained from the ambient acceleration measurements through WSSNs using both FDD and SSI. The natural frequencies currently identified using the WSSNs are found to be in excellent agreements with those previously obtained by the existing wired sensors. The extracted mode shapes show excellent agreements with those from the FE analysis.

5) By utilizing the symmetric property of slender cables with small sag, the natural frequencies of cables are extracted by frequency-domain analysis from the measured 3-axis acceleration data with complementary use of the lateral vibration data of the cables. Tension forces of 10 cables estimated from the natural frequencies of the cable using a vibration method are found to be very close to those from 2 previous regular inspections within 4% difference.

6) A substructural damage identification method for a cable-stayed bridge is now under development with full utilization of decentralized computing capability through WSSN. Substructural modal information of the deck/pylon and cable tension forces may be effectively combined to assess the comprehensive structural integrity of the bridge structure.

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