Hybrid Sensor-Camera Monitoring for Damage Detection: Case Study of a Real Bridge
Publication: Journal of Bridge Engineering
Volume 21, Issue 6
Abstract
This article presents the real-world implementation of a novel monitoring system in which video images and conventional sensor network data are simultaneously analyzed to detect possible damage on a movable bridge. The monitoring system was designed to detect such problems at the onset of damage. A video stream of traffic is processed to detect and classify vehicles to determine the vehicle load and location, while strain measurements are simultaneously collected at various critical locations on the bridge for both normal and damage conditions. A series of unit influence lines can then be extracted for all of the scenarios using the image and sensor data. Because large data sets result from continuous monitoring, the system also includes a statistical outlier-detection algorithm. The proposed methodology was successfully used to detect and locate common damage scenarios on a real-world bascule bridge.
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References
Abdel-Qader, I., Abudayyeh, O., and Kelly, M. E. (2003). “Analysis of edge-detection techniques for crack identification in bridges.” J. Comput. Civil Eng., 255–263.
Busca, G., Cigada, A., Mazzoleni, P., Tarabini, M., and Zappa, E. (2013). “Static and dynamic monitoring of bridges by means of vision-based measuring system.” Topics in Dynamics of Bridges, Vol. 3, Springer, New York, 83–92.
Catbas, F. N., Gul, M., Gokce, H. B., Zaurin, R., Frangopol, D. M., and Grimmelsman, K. A. (2014). “Critical issues, condition assessment and monitoring of heavy movable structures: Emphasis on movable bridges.” Struct. Infrastruct. Eng., 10(2), 261–276.
Catbas, F. N., et al. (2010). “Long term bridge maintenance monitoring demonstration on a movable bridge: A framework for structural health monitoring of movable bridges.” Final Rep. BDK78 977-07, Florida Dept. of Transportation, Tallahassee, FL.
Catbas, F. N., Zaurin, R., Gul, M., and Gokce, H. B. (2012). “Sensor networks, computer imaging, and unit influence lines for structural health monitoring: Case study for bridge load rating.” J. Bridge Eng., 662–670.
Catbas, F. N., Zaurin, R., Susoy, M., and Gul, M. (2007). “Integrative information system design for Florida Department of Transportation: A framework for structural health monitoring of movable bridges.” Final Rep. BD548-11, Florida Dept. of Transportation, Tallahassee, FL.
Elgamal, A., et al. (2003). “Health monitoring framework for bridges and civil infrastructure.” Proc., 4th Int. Workshop on Structural Health Monitoring, F.-K. Chang, ed., DEStech, Lancaster, PA, 123–130.
Fraser, M. S. (2006). “Development and implementation of an integrated framework for structural health monitoring.” Doctoral dissertation, Univ. of California, San Diego, La Jolla, CA.
Gokce, H. B., Gul, M., and Catbas, F. N. (2012). “Implementation of structural health monitoring for movable bridges.” Transportation Research Record, 2313, 124–133.
Gul, M., Catbas, F. N., and Hattori, H. (2013). “Image-based monitoring of open gears of movable bridges for condition assessment and maintenance decision making.” J. Comput. Civil Eng., 04014034.
Jahanshahi, M. R., Jazizadeh, F., Masri, S. F., and Becerik-Gerber, B. (2012a). “A novel system for road surface monitoring using an inexpensive infrared laser sensor.” Proc., SPIE 8345, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, SPIE, San Diego.
Jahanshahi, M. R., and Masri, S. F. (2012b). “Adaptive vision-based crack detection using 3D scene reconstruction for condition assessment of structures.” Autom. Constr., 22, 567–576.
Lee, J. J., Fukuda, Y., Shinozuka, M., Yun, C.-B., and Cho, S. (2007). “Development and application of a vision-based displacement measurement system for structural health monitoring of civil structures.” Smart Struct. Syst., 3(3), 373–384.
Lee, J. J., and Shinozuka, M. (2006). “A vision-based system for remote sensing of bridge displacement.” NDT and E Int., 39(5), 425–431.
Oh, J.-K., et al. (2009). “Bridge inspection robot system with machine vision.” Autom. Constr., 18(7), 929–941.
Phares, B., Washer, G., Rolander, D., Graybeal, B., and Moore, M. (2004). “Routine highway bridge inspection condition documentation accuracy and reliability.” J. Bridge Eng., 403–413.
Santos, C. A., Costa, C. O., and Batista, J. P. (2012). “Calibration methodology of a vision system for measuring the displacements of long-deck suspension bridges.” Struct. Control Health Monit., 19(3), 385–404.
Turer, A., Levi, A., and Aktan, A. (1998). Instrumentation, proof-testing and monitoring of three reinforced concrete deck-on-steel girder bridges prior to, during, and after superload, Univ. of Cincinnati Infrastructure Institute, Cincinnati.
Wahbeh, M. A., 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.
Yamaguchi, T., and Hashimoto, S. (2010). “Fast crack detection method for large-size concrete surface images using percolation-based image processing.” Mach. Vision Appl., 21(5), 797–809.
Zaurin, R., and Catbas, F. N. (2010a). “Integration of computer imaging and sensor data for structural health monitoring of bridges.” Smart Mater. Struct., 19(1), 015019.
Zaurin, R., and Catbas, F.N. (2010b). “Structural health monitoring using video stream, influence lines, and statistical analysis.” Struct. Health Monit., 10(3), 309–332.
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© 2016 American Society of Civil Engineers.
History
Received: Dec 18, 2014
Accepted: May 29, 2015
Published online: Feb 1, 2016
Published in print: Jun 1, 2016
Discussion open until: Jul 1, 2016
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