Sensor Networks, Computer Imaging, and Unit Influence Lines for Structural Health Monitoring: Case Study for Bridge Load Rating
Publication: Journal of Bridge Engineering
Volume 17, Issue 4
Abstract
In this paper, a novel methodology for structural health monitoring of a bridge is presented with implementations for bridge load rating using sensor and video image data from operating traffic. With this methodology, video images are analyzed by means of computer vision techniques to detect and track vehicles crossing the bridge. Traditional sensor data are correlated with computer images to extract unit influence lines (UILs). Based on laboratory studies, UILs can be extracted for a critical section with different vehicles by means of synchronized video and sensor data. The synchronized computer vision and strain measurements can be obtained for bridge load rating under operational traffic. For this, the following are presented: a real life bridge is instrumented and monitored, and the real-life data are processed under a moving load. A detailed finite-element model (FEM) of the bridge is also developed and presented along with the experimental measurements to support the applicability of the approach for load rating using UILs extracted from operating traffic. The load rating of the bridges using operational traffic in real life was validated with the FEM results of the bridge and the simulation of the operational traffic on the bridge. This approach is further proven with different vehicles captured with video and measurements. The UILs are used for load rating by multiplying the UIL vector of the critical section with the load vector from the HL-93 design truck. The load rating based on the UIL is compared with the FEM results and indicates good agreement. With this method, it is possible to extract UILs of bridges under regular traffic and obtain load rating efficiently.
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References
AASHTO. (2003). Manual for condition evaluation and load and resistance factor rating (LRFR) of highway bridges, Washington, DC.
Catbas, F. N. et al. (2010). “Long term bridge maintenance monitoring demonstration on a movable bridge.” Final Rep. No. BD548-23 (prepared for Florida Dept. of Transportation), Univ. of Central Florida, Orlando, FL.
Catbas, F. N., Shah, M., Burkett, J., and Basharat, A. (2004). “Challenges in structural health monitoring.” Proc., 4th Int. Workshop on Structural Control, Smyth, A. and Betti, R., eds., Columbia University, New York, 195–202.
Chen, Y., and Feng, M. Q. (2006). “Modeling of traffic excitation for system identification of bridge structures.” Comput. Aided Civ. Infrastruct. Eng.CCIEFR, 21(1), 57–66.
Fraser, M., and Elgamal, A. (2006). “Video and motion structural monitoring framework.” 4th China-Japan-US Symposium on Structural Control and Monitoring (CD-ROM).
Fraser, M., Elgamal, A., He, X., and Conte, J. P. (2010). “Sensor network for structural health monitoring of a highway bridge.” J. Comput. Civ. Eng., 24(1), 11–24.JCCEE5
Turer, A., Levi, A., and Aktan, A. E. (1998). Instrumentation, proof-testing and monitoring of three reinforced concrete deck-on-steel girder bridges prior to, during, and after superload, Cincinnati Infrastructure Institute, Cincinnati.
Wahbeh, A. M., Caffrey, J. P., and Masri, S. F. (2003). “A vision-based approach for the direct measurement of displacements in vibrating systems.” Smart Mater. Struct., 12(5), 785–794.SMSTER
Zaurin, R., and Catbas, F. N. (2007). “Computer vision oriented framework for structural health monitoring of bridges.” Proc., IMAC XXV, Society for Experimental Mechanics, Orlando, FL.
Zaurin, R., and Catbas, F. N. (2010a). “Integration of computer imaging and sensor data for structural health monitoring of bridges.” Smart Mater. Struct.SMSTER, 19(1), 015019.
Zaurin, R., and Catbas, F. N. (2010b). “Structural health monitoring using computer vision and influence lines.” Struct. Health Monit., 10(3), 309–332.
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© 2012. American Society of Civil Engineers.
History
Received: Aug 23, 2010
Accepted: Jul 26, 2011
Published online: Dec 7, 2011
Published in print: Jul 1, 2012
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