2 Application of an Automated Wireless Structural Monitoring System for Long-span Suspension Bridges

8/19/2019 2 Application of an Automated Wireless Structural Monitoring System for Long-span Suspension Bridges

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APPLICATION OF AN AUTOMATED WIRELESS STRUCTURAL

MONITORING SYSTEM FOR LONG-SPAN SUSPENSION BRIDGES

M. Kurata1, J. P. Lynch1, G. W. van der Linden2, P. Hipley,3 and L.-H. Sheng3

1Department of Civil and Environ. Eng., University of Michigan, Ann Arbor, MI 481052SC Solutions, Sunnyvale, CA 940853California Department of Transportation (Caltrans), Sacramento, CA 95816

ABSTRACT. This paper describes an automated wireless structural monitoring system installed atthe 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 installedon 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 thesensors 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, thefundamental 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 deliveryrate) 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 theindividual 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 ., sensornodes, radios, data logging servers) must be durable under exposure to extreme weatherconditions.

Review of Progress in Quantitative Nondestructive Evaluation, Volume 30

AIP Conf. Proc. 1335, 33-40 (2011); doi: 10.1063/1.3582777© 2011 American Institute of Physics 978-0-7354-0888-3/$30.00

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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 CarquinezBridge, 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, long-

term 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 long-

span bridges [e.g., 1, 2]. At the New Carquinez Bridge site, communication stability andsystem 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 serverremotely 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 differentsystem 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 in-network to compress the amount of information to be communicated and stored by thesystem. 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 isalso accessible via the internet-based cyber-environment. Within the cyber-environment

are a series of powerful data interrogation servers. Specifically, servers hosting modelingtools (e.g ., finite element solvers), damage detection tools, and system identification toolscan 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 bythe 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 butthe 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 nodedesign. 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 bridgestructures for long periods of time without human intervention:

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The node has an enhanced transmission range through the use of a power-amplified

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 energyharvesting 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 databasestored in a remote central repository using a Client-Server model. The database serverconsists 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

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.

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layer supports GUIs (graphical user interfaces) for reporting monitoring systeminformation 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 andthe “ server” manages the central resources of the data server and controls the remoteaccesses 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 serverwhich 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 systemidentification 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 andsupport 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 1056m with the main span (i.e., the span between the two towers) being 728 m long. Theinspectors of the California Department of Transportation (Caltrans) visually check the

bridge for structural deficiencies and corrosion on a bi-annual cycle. The itemized list ofinspection 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 seismicmonitoring installed prior to the bridge opening [6]. Hong et al . [7] used the NCB as atestbed for the application of a computational framework that numerically predicts thewind-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 uni-directional 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 threequarter 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 wasexperienced 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

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(a)

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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.

-1

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1 FE Model:0.212sec Experiment:0.194sec MAC:0.910

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Bridge main span (m)

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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.

A c c e l e r a t i o n ( m g )

P o w e r / f r e q u e n c y ( d B / H z )


P o w e r / f r e q u e n c y ( d B / H z )


A c c e l e r a t i o

n ( m g )

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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 thefrequency-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 long-

term 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 omni-directional 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 sinceJune 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, low- power 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 cyber-environment) 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 robustcontinuous operation with automated rebooting. The Narada server program automaticallystarts 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.

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Narada nodes. After data collection, the server program triggers the Narada sleep modeand 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 wirelesssensor network with an internet-enabled cyber-environment for ensuring periodic data

collection and automated secured data transfer into a remote database server. The Client -Server model featured by the cyber-environment manages data transfer and storage in thefederated relational data repository and enables easy access to the stored data by

applications engaged in data processing and mining. The wireless sensor nodes have beenmodified 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 isunderway with several upgrades and system expansions scheduled through the year 2010

and 2011.

ACKNOWLEDGEMENTS

The authors would like to gratefully acknowledge the generous support offered bythe U.S. Department of Commerce, National Institute of Standards and Technology

(NIST) Technology Innovation Program (TIP) under Cooperative Agreement Number70NANB9H9008. 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 EnergyHarvesting 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 wirelesssensors”, 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).

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2 Application of an Automated Wireless Structural Monitoring System for Long-span Suspension Bridges