Structural Health Monitoring of Bridges Using Wireless Sensor Networks

Structural Health Monitoring of Bridges Using Wireless Sensor Networks Tyler Harms, Sahra Sedigh, and Filippo Bastianini

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ging and degradation of transportation infrastructure pose significant safety concerns, especially in light of increased use of these structures. The economic downturn further exacerbates such concerns, especially for critical structures such as bridges, where replacement is infeasible and maintenance and repair are expensive. The US Federal Highway Administration has classified over 25% of the bridges in the United States as either structurally deficient or functionally obsolete, underscoring the importance of structural health monitoring (SHM) to ensure public safety. We give an overview of emerging wireless sensor networks (WSN) for autonomous SHM systems, their application, the power use and sources needed to support autonomy, and the type of communication that allows remote monitoring.

Background Traditional SHM, which requires an onsite evaluator, is prohibitively expensive for all but a small fraction of structures and also suffers from the significant drawback of subjectivity. For these reasons, autonomous SHM has emerged as an increasingly active research area. Several wired SHM systems have been developed, but they suffer from high cost, inadequate design, difficult installation, or some combination of these shortcomings. Their high power consumption constrains their deployment to locations with access to the power grid, as alternative or portable power sources are rarely adequate. A more important constraint associated with the use of wired SHM systems is the wiring required to supply power and interconnect components of the system. This difficulty in retrofitting hampers their utility.

Several existing SHM systems use wireless communication to allow devices to coordinate and collaborate to more effectively measure a structure, whether the goal is improved spatial resolution [1], network resilience [2], or advanced in-situ analysis [3]. The majority of these systems are based on general-purpose sensing platforms, or motes, such as the Tmote Sky by Moteiv, and Mica and MicaZ by Crossbow. They provide basic sensing functionality but are largely unsuited for long-term installation on civil structures. These commercial network devices greatly reduce development time, as little, if any, hardware design is required. It is then possible to devote the time saved to developing and optimizing software. Each of these motes supports TinyOS, which is a comprehensive software solution for wireless sensor networks, allowing rapid development at the cost of losing direct control over the hardware. In mote-based SHM systems, basic functionality can be quickly attained, and the system can be gradually improved wherever possible. The systems are typically capable of communicating wirelessly over a range of 100 m, using a 2.4 GHz 802.15.4 transceiver. Field testing of a number of these systems provides strong evidence of the effectiveness of wireless SHM, with results reported within 0.1% of those measured using wired sensors for vibration analysis [4]. However, most of these systems suffer from high power consumption, yet they are generally equipped with a limited power supply. Even under the most stringent power management, these motes have an unattended life of approximately one year. The motes are also limited in the number of I/O connections available for sensors, meaning that numerous motes are necessary if more than a few spatial data points are needed. Almost all of

The US Federal Highway Administration has classified over 25% of the bridges in the United States as either structurally deficient or functionally obsolete, underscoring the importance of structural health monitoring (SHM) to ensure public safety.

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these systems use a laptop or base station to aggregate data from the sensor nodes, and the networks typically lack a mechanism for remotely communicating the measured data without access to the power grid and costly communication hardware. This reliance on wires for communications or power restricts the number of potential installation locations, as many bridges, especially in rural areas, have no utilities on-site. A specific example of these problems is detailed in [5] which describes the installation of a sensor network controlled by a laptop computer on a bridge in Connecticut. The intent was to power the laptop using a solar panel and transmit vibration Fig. 1. Block Diagram of the SmartBrick Platform [6]. (© 2009 IEEE, Proc. of 12th IEEE Int’l. Conf. on Intelligent Transportation Systems, used with permission.) data to a remote server, but the excessive power consumption of the laptop far exceeded the solar panel’s output and caused infrastructure. Ultra-low power consumption and redundant power supplies along with this communication capability the system to fail prematurely. Although most wireless SHM systems use ZigBee (or allow the system to operate completely wirelessly while proanother 802.15.4-based communication protocol), which is viding full remote monitoring, maintenance, and calibration ideal for low-power, low-data rate communications, there are capabilities. In the interest of more efficient monitoring of larger struca few projects that employ Bluetooth or Wi-Fi to communicate within the network. Both of these technologies work in the tures, the SmartBrick base station was supplemented with same frequency range and provide better data throughput sensor nodes that are similar to it in sensing capabilities but than ZigBee but with much higher power consumption. Wi-Fi lack the modem, which is the most expensive hardware comhas a significantly larger transmission range and supports a ponent. Short-range, low-power wireless ZigBee transceivers large number of nodes, but the power requirements are very high compared to low-power technologies such as 802.15.4/ ZigBee. The use of Wi-Fi or Bluetooth is justifiable in some sensing applications, e.g., high-frequency vibrational analysis, but is rarely appropriate for sensor networks.

The SmartBrick Base Platform Recent years have witnessed the development of wireless SHM systems specifically designed for low power consumption and long-range communication, both of which facilitate autonomous and remote monitoring. An example is the SmartBrick platform [6] which we developed. It is a completely wireless and fully autonomous system for SHM. The heart of the system is the SmartBrick base station that offers extensive SHM capabilities, including onboard and external sensors for measurement of environmental and structural phenomena such as temperature, strain, tilt, and vibration. Possibly the most important feature of the SmartBrick base station is the embedded quad-band modem for mobile communications/ general packet radio service (GSM/GPRS) which is used for bidirectional long-range communication over the cellular phone December 2010

Fig. 2. Deployment on bridge A6531 in Osage Beach, MO

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link these nodes to the base station and to each other. Extensive I/O and several expansion headers are provided for the base station and sensor nodes, enabling the addition of virtually any type of digital or analog sensor and facilitating control of external devices such as actuators. Other systems utilizing wireless sensor networks for SHM of bridges provide similar capabilities. The block diagram in Fig. 1 depicts the SmartBrick network. The base station and sensor nodes collect data from the onboard and external sensors. The sensor nodes communicate their data to the base station over the ZigBee connection. The base station Fig. 3. Data flow in the SmartBrick wireless sensor network [2]. (© 2009 IEEE, Proc. of 12th IEEE Int’l. Conf. on processes these data and com- Intelligent Transportation Systems, used with permission.) municates them, along with any alerts generated, to a number of destinations over the abnormalities in trend or level. The detection of any abnorGSM/GPRS link provided by the cellular phone infrastructure. mality generates an “alert” condition where a message is The data are reported by email and FTP to redundant servers, broadcast through the redundant channels mentioned above, via the Internet, at regular intervals or on an event-triggered and the data are uploaded to the server. basis. The alerts are sent directly by SMS text messaging and by In contrast, the data collected by seismic detectors because of email. A web-based graphical user interface (GUI) is provided an earthquake can change very rapidly, and such a change can for download of data and charts, supported by a processing indicate a significant safety hazard. These sudden changes trigbackend. Fig. 2 shows a sample deployment. ger event-based data collection from the seismic sensor and from other sensors that could corroborate the existence of a threat. Rapid changes and fluctuations in the values of the parameters measured result in a large volume of data whose transmission in raw form would be prohibitively expensive in terms of both power and memory, which are scarce resources for any wireless device. Fast Fourier transform processing and thresholding are carried out to compress the data before writing it to memory or reporting it to remote recipients. Similar to the time-based case, the data is then scanned for abnormalities, alerts are generated if necessary, and the data is uploaded to remote servers. It is worth noting that data acquisition from one or both categories of sensors can be carried out on-demand as a result of requests issued from the web interface. Fig. 3 depicts the data flow in the SmartBrick platform which is typical of most wireless sensor networks used for Other SHM Systems SHM applications. As seen in the figure, the collection of Other noteworthy studies include [1], in which the authors data from the sensors can be time-or event-triggered. Time- describe their development of a wireless sensor system (WSS) triggered data acquisition takes place at regular intervals solution to serve as a universal platform for high-rate, largewhich can be specified remotely through the web interface. scale monitoring of structural response. Tmote Sky motes Data from quasi-static sensors, e.g., temperature sensors and serve as the basis for wireless sensor nodes that incorporate strain gauges, are collected in this fashion. The parameters an accelerometer, strain transducer, temperature sensor and monitored by these sensors are not subject to rapid or sud- analog-to-digital converter, along with signal conditioning den changes, and periodic collection of their values suffices. circuitry. Similar to the SmartBrick platform, the system uses a The acquired data are written to memory and scanned for low-power microcontroller, an onboard transceiver operating

These commercial network devices greatly reduce development time, as little, if any, hardware design is required. It is then possible to devote the time saved to developing and optimizing software.

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in the 2.4 GHz range, albeit with a custom radio protocol, and has a comparable outdoor range of approximately 100 m. The main difference between the two platforms is in their power consumption; the use of commercial motes in [1] leads to higher power consumption and, consequently, to lower autonomous lifetime. The Imote2 platform has facilitated the application of wireless sensor networks to several SHM applications as it provides a base platform to which various sensor boards can be interfaced [3]. The sensor nodes perform the majority of the computational tasks, thus limiting the power consumed during data transmission. Similar to the SmartBrick and the WSS, the Imote2 sensor node employs a low-power processor with variable speed and 802.15.4-compliant radio with onboard antenna. The sensor nodes can be used to support the high sampling frequency required for dynamic SHM, with data rates comparable to those of the aforementioned two platforms (in the hundreds of kbps). In contrast to the SmartBrick, which employs a purpose-built operating system designed for low-power consumption, the Imote2 platform employs TinyOS, as does the WSS. The SHM-A (accelerometer) sensor board consists of a 3-axis accelerometer, low-pass filter, gain difference amplifier and an analog-to-digital converter. Applications of the Imote2 platform include monitoring of the Jindo Bridge in South Korea where the system was supplemented to include a sensor board for measurement of wind speed – a relevant parameter for the cable-stayed bridge [3]. A gateway node was used to transmit/receive data from/to sensors on the bridge. The two-month battery life of the system is extended through the use of solar panels for power harvesting. An open source tool suite is available for the Imote2 platform and can be used to implement modal identification and damage detection algorithms. Similar to the SmartBrick and WSS, physical robustness of this platform is ensured by an IP68-compliant enclosure that protects the sensor nodes and base station against dust, corrosion, and humidity. The communication capabilities of the systems that we described can be leveraged to further improve the safety of the traveling public. We are developing a system that utilizes the SmartBrick to directly contact interested parties in case of hazardous conditions on or near the structure being monitored. The system will allow users to voluntarily and privately register for alerts from one or more specific devices in their area. The system can then send an alert via e-mail or text message to subscribers, providing them with potentially critical information regarding the safety of their travel. In the slightly longer term, an enhanced web interface is in development that will provide additional functionality, e.g., access to data logs by subscribers via the Internet.

Acknowledgments The authors gratefully acknowledge the support of the United States Department of Transportation, the Washington County Commission, the Missouri University of Science and Technology Intelligent Systems Center, and the EU FP7 Program on Smart Monitoring of Historic Structures.

References [1] M. J. Whelan and K. D. Janoyan, “Design of a robust, highrate wireless sensor network for static and dynamic structural monitoring,” Journal of Intelligent Material Systems and Structures, vol. 20, pp. 849-863, May 2009. [2] H. Ide, F. Abdi, R. Miraj, C. Dang, T. Takahashi, and B. Sauer, “Wireless-Zigbee strain gage sensor system for structural health monitoring” in Proc. of SPIE, Photonics in the Transportation Industry: Auto to Aerospace II, San Diego, vol. 7314, pp. 731403731412, 2009. [3] S. Cho, S. A. Jang, H. Jo, K. Mechitov, J. A. Rice, H. J. Jung, C. B. Yun, B. F. Spencer, T. Nagayama, and J. Seo, “Structural health monitoring system of a cable-stayed bridge using a dense array of scalable smart sensor network,” in Proc. of SPIE, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, pp. 764707-764712, 2010. [4] M. J. Whelan, M. V. Gangone, K. D. Janoyan, K. Cross, and R. Jha, “Reliable high-rate bridge monitoring using dense wireless sensor arrays,” in Proc. 6th Int’l. Workshop on Structural Health Monitoring 2007, pp. 1207–1215. [5] J. K. Olund, A. J. Cardini, G. P. J. Troiano, C. Liu, E. Feldblum, P. D. Attilio, and J. T. DeWolf, “Development and implementation of a solar powered wireless monitoring system on a truss bridge in Connecticut,” in Proc. 6th Int’l. Workshop on Structural Health Monitoring 2007 pp. 1174-1181, 2007. [6] T. Harms, F. Bastianini, and S. Sedigh, “Recent enhancements to the SmartBrick structural health monitoring platform,” in Proc. of

Summary

12th IEEE Int’l. Conf. on Intelligent Transportation Systems (ITSC), St.

The applications of wireless sensor networks in SHM continue to expand in capabilities and performance. Short-range communication has been used to increase the coverage area of monitoring systems, and long-range communication provides December 2010

a means for reporting data, providing alerts, and delivery of software upgrades. Systems utilizing commercial sensor nodes are undergoing continual improvements targeted at lowering their high power consumption – the main shortcoming of these systems. Wireless sensor networks are the key enabler of the most reliable and durable systems for long-term SHM and have the potential to dramatically increase public safety by providing early warning of impending structural hazards. The cost reduction achieved by these systems has the potential to expand the practice of SHM to a significantly higher number of existing and new infrastructures. This improvement will increase safety and reduce the cost of operations by facilitating real-time monitoring and yield a more efficient maintenance schedule that extends the useful life of a wide range of infrastructures.

Louis, MO, pp. 1-6, 2009.

Sahra Sedigh ([email protected]) is an Assistant Professor of Electrical and Computer Engineering with a joint


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appointment in Computer Science and a Research Investigator with the Intelligent Systems Center at the Missouri University of Science and Technology. She received the B.S. degree from Sharif University of Technology and the M.S. and Ph.D. degrees from Purdue University in electrical engineering. Her projects include research on dependability of the electric power grid, large-scale water distribution networks, and transportation infrastructures, including an instrumented bridge test bed funded by the US and Missouri Departments of Transportation. She was selected as one of 49 participants in the National Academy of Engineering’s First Frontiers of Engineering Education Symposium in Nov. 2009.

Filippo Bastianini is the Chief Technology Officer of Sestosensor, SRL where he is engaged in the development of innovative solutions for intelligent structural health monitoring. He received the B. Eng. degree in nuclear engineering from the Università di Bologna and the Ph.D. in composite materials from the Università degli Studi di Lecce. Tyler Harms is a software engineer at Garmin International working on embedded 3-D graphics for portable navigation devices. He received the B.S. and M.S. degrees in Computer Engineering from the Missouri University of Science and Technology. His M.S. research included development of the SmartBrick platform for wireless structural health monitoring.

For additional information and technical details, see our sister publication, the IEEE Transactions on Instrumentation and Measurement. F. G. Baptista and J. V. Filho, “A new impedance measurement system for PZT-based structural health monitoring,” IEEE Trans. on Instrum. and Meas., vol. 58, no. 10, pp. 3602-3608, Oct. 2009. D. Wang and W. Liao, “Wireless transmission for health monitoring of large structures,” IEEE Trans. on Instrum. and Meas., vol. 55, no. 3, pp. 972-981, June 2006.

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