Author Instructions

APPLICATION OF AN AUTOMATED WIRELESS STRUCTURAL MONITORING SYSTEM FOR LONG-SPAN SUSPENSION BRIDGES M. Kurata1, J. P. Lynch1, G. W. van der Linden2, P. Hipley3 and L.-H. Sheng3 1

Department of Civil and Environ. Eng., University of Michigan, Ann Arbor, MI 48105 SC Solutions, Sunnyvale, CA 94085 3 California Department of Transportation (Caltrans), Sacramento, CA 95816 2

ABSTRACT. This paper describes an automated wireless structural monitoring system installed at the New Carquinez Bridge (NCB). The designed system utilizes a dense network of wireless sensors installed in the bridge but remotely controlled by a hierarchically designed cyber-environment. The early efforts have included performance verification of a dense network of wireless sensors installed on the bridge and the establishment of a cellular gateway to the system for remote access from the internet. Acceleration of the main bridge span was the primary focus of the initial field deployment of the wireless monitoring system. An additional focus of the study is on ensuring wireless sensors can survive for long periods without human intervention. Toward this end, the life-expectancy of the wireless sensors has been enhanced by embedding efficient power management schemes in the sensors while integrating solar panels for power harvesting. The dynamic characteristics of the NCB under daily traffic and wind loads were extracted from the vibration response of the bridge deck and towers. These results have been compared to a high-fidelity finite element model of the bridge. Keywords: wireless sensors, structural health monitoring, autonomous monitoring, power management PACS: 07.07.Df, 07.07Hj, 07.05.Hd, 07.05.Tp

INTRODUCTION Long-term health monitoring of long-span bridges is a major challenge for wireless sensing technology. Wireless sensing systems are desirable because of their substantially reduced installation costs when compared to tethered sensing systems. However, the fundamental challenge associated with wireless telemetry in such a harsh environment lies in their long-term reliability and robustness. For example, wireless communication within the bridge monitoring system needs to be extremely robust (e.g., a near 100% data delivery rate) to reliably collect data from a dense array of wireless sensors. The existence of a few nodes with poor communications may delay the collection of data or lead to data losses which undermine the integrity of the collected dataset. Furthermore, the battery life of the individual wireless sensors must be long enough to ensure that regular battery replacement does not erode the life-cycle cost savings offered by the eradication of cabling in the monitoring system. Finally, all of the wireless monitoring system components (e.g., sensor nodes, radios, data logging servers) must be durable under exposure to extreme weather conditions.

While many wireless bridge monitoring systems have been deployed over short periods, few long-term wireless monitoring systems have been installed on long-span suspension bridges. Toward this end, this paper describes the early deployment of an automated wireless sensor network on a long-span suspension bridge (New Carquinez Bridge, Vallejo, CA). The wireless monitoring system includes access to the internet to facilitate the creation of a more grandiose cyber-environment whose architecture is customized to the decision making process of the bridge owner. The proposed wireless monitoring system is designed to be reliable with enhanced communication ranges, longterm power management strategies, and to operate on solar energy harvesting. In addition, the system is designed to be flexible by allowing system reconfiguration and remote access via the internet. The design of such a wireless system is not limited to this study alone. For example, recent studies on bridges in East Asia are aimed towards specifically addressing the long-term reliability and performance of wireless sensing systems on longspan bridges [e.g., 1, 2]. At the New Carquinez Bridge site, communication stability and system robustness were first verified through a short-term deployment of a dense wireless sensor network at various locations on the bridge. This was followed by efforts to add permanent wireless sensors to the bridge for long-term continuous monitoring. In addition, the system is fully automated to transfer bridge response data to a database server remotely located in Sunnyvale, California. LONG-TERM WIRELESS STRUCTURAL MONITORING SYSTEM DESIGN Hierarchical System Design The architectural design of the wireless structural monitoring system is hierarchically structured with system functionality and power usage delineated on different system tiers [Figure 1]. In the lower tier, low-power wireless sensor nodes collect data (e.g., vibration responses, atmospheric profiles, strain hysteresis) and process data innetwork to compress the amount of information to be communicated and stored by the system. The processed data is transmitted by the wireless sensors on the lower tier to the upper tier where a central data repository stores the data within a server; the repository is also accessible via the internet-based cyber-environment. Within the cyber-environment are a series of powerful data interrogation servers. Specifically, servers hosting modeling tools (e.g., finite element solvers), damage detection tools, and system identification tools can freely connect to the database of bridge response data. The system end-user is then provided decision making tools to understand and interpret the information generated by the data interrogation servers. Lower Tier: Long-Range Wireless Monitoring with In-Network Data Interrogation The Narada wireless nodes [Figure 2a] are used to create the lower tier. This tier has been successfully implemented on various civil infrastructure systems in the past [7]. The node offers high-quality data collection capabilities with its 16-bit digital resolution and 100 kHz sample rate. Typically, accelerometers are interfaced to the Narada node but the unit can accept strain gages, displacement sensors, thermometers, among other sensors. Once collected, data can be stored in the 128 kB memory bank included in the node design. The collected data is transmitted via the 2.4 GHz IEEE802.15.4 radio standard using the Texas Instruments CC2420 transceiver. Some additional features have recently been implemented on the Narada node to further enhance its use in long-span bridge structures for long periods of time without human intervention:

FIGURE 1. Two-tiered automate wireless structural monitoring system.

(a) (b) FIGURE 2. (a) low-power Narada wireless node; (b) pre-assembled sensing unit in all-weather container.

• • • • • •

The node has an enhanced transmission range through the use of a poweramplified CC2420 radio custom fabricated for the Narada node [Figure 2a]. The node includes power management software that reduces power consumption of the unit by 40% by toggling between active (.375W) and sleep (.215W) modes. The battery life of the node is enhanced through the use of a solar energy harvesting system. Power is saved by using the embedded microcontroller to process high-rate raw data into low-rate information; less communication translates into power saved. The node is assembled into an all-weather package for quick and accurate installation [Figure 2b]. A high-gain, omni-directional antenna at the data server receiver enhances the reliability of the monitoring system when deployed over large spatial areas.

Upper Tier: Automated Data Transfer with Client-Server model The cyberenvironment designed on the upper tier automates the collection of data on the bridge (e.g., on a schedule) and the transfer of data to a federated relational database stored in a remote central repository using a Client-Server model. The database server consists of three layers: 1) data layer stores data and data logic; 2) application layer receives and publishes data for data processing and decision support tools; 3) presentation

layer supports GUIs (graphical user interfaces) for reporting monitoring system information and for alerting bridge owners. In the Client-Server model, the “clients” (e.g., wireless sensor network receiver, data interrogation tools, etc.) provide and use data and the “server” manages the central resources of the data server and controls the remote accesses of the clients. For example, the monitoring system receiver at the bridge, as a client, collects data from Narada sensors and initiates the transfer of data to the server which resides physically in the database server off-site. This procedure-driven access model is suitable for user-interaction and data-mining while hiding data from other users. The application layer supports the publishing of data; applications such as system identification and damage detection can utilize the data at the application layer. The presentation layer reports information to bridge owners, inspectors, and other end-users. LONG-SPAN SUSPENSION BRIDGE TESTBED The New Carquinez Bridge (NCB) is a long-span suspension bridge located 32 km northeast of San Francisco and carries westbound I-80 traffic across the Carquinez Strait. Two main towers are made of hollow concrete sections linked at the tower top and at the main deck level. The suspension cables are anchored at the north and south end and support the dead and live vertical loads of the main deck by hanger cables. The main deck consists of a steel orthotropic box girder that was constructed overseas and shipped to the bridge site were deck sections were welded together. The total length of the bridge is 1056 m with the main span (i.e., the span between the two towers) being 728 m long. The inspectors of the California Department of Transportation (Caltrans) visually check the bridge for structural deficiencies and corrosion on a bi-annual cycle. The itemized list of inspection tasks include assessment of the road pavement condition, the weld condition inside the girder, the surface condition underneath the girder, the concrete condition of the pylon and link beams, the integrity of the paint on the main suspension cables and hanger cable, the suspension cable alignment, and the corrosion state of wires in the two anchorage rooms. Recently, two research teams have conducted dynamic studies (i.e., modal analysis) of the NCB using bridge response data collected using a permanent seismic monitoring installed prior to the bridge opening [6]. Hong et al. [7] used the NCB as a testbed for the application of a computational framework that numerically predicts the wind-excited response of the suspension bridge. Specifically, the researchers updated a finite element method (FEM) model using the dynamic properties extracted from the seismic monitoring system.

SHORT TERM DEPLOYMENT OF THE WIRELESS MONITORING SYSTEM Before permanently deploying the wireless sensors, the radio frequency (RF) environment of the bridge needed to be assessed. Hence, long-range communication tests of the Narada sensor node were conducted throughout the NCB using a 9 dBi unidirectional antennas both for the node and the receiver. Testing was repeated at three locations on the bridge to determine an optimal permanent location for the nodes and receiver. First, when the receiver was positioned on the tower top, data collection succeeded with Narada nodes placed on the top of the main deck as far away as three quarter of the main span (approx. 700m). The strength of the wireless signal was sensitive to the location of the Narada antennas; in this case, mild signal interference was experienced when the nodes were placed too close to the main deck railings and light

poles. Second, the wireless sensors and receiver were taken inside the steel girder for testing. In general, wireless communication inside the steel girder was challenging due to the thick steel diaphragms impeding the radio signal; communication ranges of 50 m or less were experienced. Third, the Narada wireless sensor nodes were positioned underneath the bridge girder along the main span with the receiver located at the tower underneath the girder. Communication between the receiver and the nodes reliably reached as far as three quarter of the main span (540m). Based on these tests, it was decided that the optimal location for the Narada wireless sensors during the long-term deployment would on be the underside of the main girder. This location would offer reliable wireless communications with a receiver located underneath the main span installed on the tower link beam. To obtain the modal properties of the bridge, a short-term deployment of Narada wireless sensors with MEMS accelerometers (Crossbow CXL02) interfaced was conducted. Eleven nodes with vertically oriented Crossbow CXL02 accelerometers interfaced were magnetically mounted to the top surface of the steel deck on the north side of the bridge [Figure 3a]. The receiver station was temporary installed at the mid-span of the deck. The sensors were packaged in rain-proof containers. The units operated continuously for 2 days under harsh winter weather conditions (e.g., rain, fog, cold night temperatures) when powered by 5 lithium ion AA batteries. The vertical vibration response of the main deck was measured with sample rates of 50, 100 and 200 Hz and for collection periods of 50, 100 and 200 sec. Figure 3b shows typical acceleration time histories and their corresponding power spectral density (PSD) functions derived off-line using a nonparametric method (i.e., periodogram). The maximum acceleration in the vertical direction was around 100 mg on the main deck during testing. The sensor nodes succeeded in extracting modal frequencies of the deck which appeared repeatedly in the PSD plots. The sensor nodes with tri-axial accelerometers at the top of two towers (North and South towers) also succeeded in identifying the modal frequencies of the towers. The California Strong Motion Instrumentation Program (CSMIP) has installed a permanent seismic monitoring system in the bridge. The system consists of tethered force balanced accelerometers installed inside the girder. To verify the functionality of the Narada wireless sensor nodes, the California Geological Survey (CGS) triggered their sensors during the field study so that CSMIP and Narada wireless sensor outputs could be compared. For these tests, the Narada nodes were mounted close to CSMIP sensors on the top surface the deck at the mid-span and inside girder at the end span. Note that the corresponding CSMIP sensors at these locations were both mounted inside the girder. The acceleration time histories revealed very good agreement between the two independent set of sensors [Figure 3c]. A high-fidelity finite element (FE) model of the NCB has also been constructed based on the as-built drawings provided by Caltrans [Figure 4a]. The details of the FE model were refined during its creation by performing a construction sequence analysis of the bridge to obtain realistic representation of residual stresses in key bridge elements after the application of the bridge dead load. Soil properties and stiffness reduction of the two concrete towers due to cracking were estimated to be negligible and were not accounted for in the model. A mesh sensitivity analysis was performed to determine the appropriate mesh fineness. The resulted model has 155,619 degrees of freedom with 159 truss elements, 14 spring elements and 19446 isotropic and orthotropic shell elements. The FE model was created using ADINA, a commercial finite element package widely used in the civil engineering community.

100

50

100 0 -100

0

50 Time (s)

100

Node # 6

0

0

0.5

1

4000 2000 0

0

0.5 1 Frequency (Hz)

100 0

-100

100

50

0

100 0

-100

0

100

50 Time (s)

Power/frequency (dB/Hz)

0

2000

Acceleration (mg)

-100

(a) 4000

Node # 8

0

Power/frequency (dB/Hz)

Node # 4 Node # 2 Acceleration (mg)

100

4000 2000 0

1

0.5

0

4000 2000 0

0

1 0.5 Frequency (Hz)

(b) Inside girder: transverse direction Narada

20 0 -20 0

20

40

60

80

0 -20 0

20

40 60 Time (s)

80

100

0

-10

100

20

Acceleration (mg)

10

CSMIP

CSMIP Narada Acceleration (mg)

Main deck mid span: vertical direction

0

10

20

30

40

50

60

0

10

20

30 Time (s)

40

50

60

10 0

-10

(c) FIGURE 3. (a) Short-term Narada deployment to monitor NCB vertical deck acceleration; (b) vibration response and their corresponding PSD functions at Nodes 2, 4, 6 and 8; (c) comparison with CSMIP sensors.

FE Model:0.212sec Experiment:0.194sec MAC:0.910 1 0 -1 FE Model:0.271sec Experiment:0.255sec MAC:0.929 1

(a) 10

FE model Experiment

0

5

SVD of PSD

-1 FE Model:0.365sec Experiment:0.351sec MAC:0.968 1 10

4

0 -1

3

10 -2 10

-1

10 Frequency [Hz]

(b)

10

0

0

100

200

300 400 500 Bridge main span (m)

600

700

800

(c)

FIGURE 4. (a) High-fidelity FE model of the NCB; (b) singular value decomposition of the estimated power spectral density function; (c) mode shape comparison between ambient vibration test and FE model.

Output only modal analysis was employed to extract the modal properties of the main deck using the ambient vibration response data obtained at the NCB site. Singular value decomposition (SVD) of the output spectrum matrix was computed using the frequency-domain decomposition (FDD) method [8]. The modal frequencies in the vertical vibration of the main deck appeared as clear peaks in the singular value plot of the power spectral density (PSD) function [Figure 4b]. The first 3 mode shapes estimated by FDD for the vertical motion of the main deck agreed well with those estimated by the FE model [Figure 4c]. The key parameters in the FE model (e.g., material properties, geometry and boundary condition) will be updated once a dense array of sensors are deployed over the bridge. DEPLOYMENT OF A LONG-TERM AUTONOMOUS MONITORING SYSTEM The team selected the underside of the main deck for the deployment of the longterm wireless sensor nodes. Similarly, the wireless monitoring system receiver station was selected for installation underneath the girder at the south tower link beam. First, a dense sensor array of 12 Narada wireless sensors, each with a tri-axial accelerometer interfaced (Crossbox CVL02TG), were magnetically mounted to the bottom surface of the steel girder [Figure 5a]. The communication stability was improved when a high-gain omnidirectional antenna (9 dBi) was carefully positioned at the tower for the receiver. Unattended operation of the Narada nodes and the receiver confirmed the sound performance of the current system configuration underneath the girder. To ensure longevity, the 12 Narada nodes were integrated with a solar energy harvesting system, i.e., 3.3W solar panel, a low-power energy charging circuit board and a rechargeable battery pack [Figure 5b]. This long-term monitoring system has been running continuously since June 2010. The data logging system installed at the receiver station was an industrial-grade single board computer (SBC) running Linux. The SBC is designed for embedded, lowpower applications and its low-power dissipation properties permit fan-less operation over a temperature range from -40°C to 85°C. The system accesses the internet (and the cyberenvironment) through a 3G mobile phone network and grants users with remote access via a secure shell (SSH) connection. The system has been specifically designed for robust continuous operation with automated rebooting. The Narada server program automatically starts after the system starts up; at the start, the server initiates data collection from the

(a)

(b) FIGURE 5. (a) Long-term deployment underneath girder; (b) deployment plan at south part of the girder

Narada nodes. After data collection, the server program triggers the Narada sleep mode and waits for an assigned period (e.g., 1 hour) until the next scheduled data collection step. At every data collection step, the receiver station autonomously transfers data as a client to the database server which is housed at a remote location off-site. SUMMARY AND CONCLUSION An automated wireless monitoring system suitable for monitoring long-span bridges has been developed and deployed. The system integrates a low-power wireless sensor network with an internet-enabled cyber-environment for ensuring periodic data collection and automated secured data transfer into a remote database server. The ClientServer model featured by the cyber-environment manages data transfer and storage in the federated relational data repository and enables easy access to the stored data by applications engaged in data processing and mining. The wireless sensor nodes have been modified to attain long-range communication, system robustness and sustainable power management, all of which are crucial for successful long-term monitoring of large-scale civil infrastructure systems. The team has completed the early application of the system at the New Carquinez Bridge in early 2010. The assessment of the implemented system is underway with several upgrades and system expansions scheduled through the year 2010 and 2011. ACKNOLEDGEMENTS The authors would like to gratefully acknowledge the generous support offered by the U.S. Department of Commerce, National Institute of Standards and Technology (NIST) Technology Innovation Program (TIP) under Cooperative Agreement Number 70NANB9H9008. Additional support was provided by the University of Michigan and the California Department of Transportation (Caltrans). REFERENCES 1. Y. Cao and M. Wang, “Structural Behavior of a Cable Stayed Bridge Through the Use of a Long-Term Health Monitoring System”, Proc. of SPIE, Vol. 7649 (2010). 2. J. W. Park, S. Cho, H-J. Jung, C-B. Yun, S. A. Jang, H. Jo, B. F. Spencer, T. Nagayama and J-W. Seo, “Long-Term Structural Health Monitoring System of A Cable-Stayed Bridge Based On Wireless Smart Sensor Networks and Energy Harvesting Techniques”, 5th World Conf. on Struct. Cont. and Monitor. (2010). 3. J. Kim, R. A. Swartz, J. P. Lynch, J-J. Lee and C-G. Lee, “Rapid-to-deploy reconfigurable wireless structural monitoring systems using extended-range wireless sensors”, J. Smart Struct. & Sys., Techno Press, 6(5), (2010). 4. J. P. Conte, X. He, B. Moaveni, S. F. Masri, J. P. Caffrey, M. Wahbeh, F. Tasbihgoo, D. H. Whang and A. Elgamal, “Dynamic Testing of Alfred Zampa Memorial Bridge”, J. Struct. Eng., vol. 134, No. 6, pp. 1006-1015 (2008). 5. A. L. Hong, F. Ubertini and R. Betti, “Wind Analysis of A Suspension Bridge: Identification and FEM Simulation”, J. Struct. Eng., (July 2010, posted ahead of print). 6. B. Peeters and C. E. Ventura, “Comparative Study of Modal Analysis Techniques for Bridge Dynamic Characteristics”, Mech. Sys. Sig. Process., 17(5), pp. 965-988 (2003).