Smart Structures and Systems

테크노프레스 (Techno-Press)
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A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification

  • Ye, X.W. (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Ni, Y.Q. (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Wai, T.T. (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Wong, K.Y. (Highways Department, The Hong Kong SAR Government) ;
  • Zhang, X.M. (Intelligent Structural Health Monitoring R&D Centre, The Hong Kong Polytechnic University Shenzhen Research Institute) ;
  • Xu, F. (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
  • 투고 : 2013.01.12
  • 심사 : 2013.02.15
  • 발행 : 2013.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

Dynamic displacement of structures is an important index for in-service structural condition and behavior assessment, but accurate measurement of structural displacement for large-scale civil structures such as long-span bridges still remains as a challenging task. In this paper, a vision-based dynamic displacement measurement system with the use of digital image processing technology is developed, which is featured by its distinctive characteristics in non-contact, long-distance, and high-precision structural displacement measurement. The hardware of this system is mainly composed of a high-resolution industrial CCD (charge-coupled-device) digital camera and an extended-range zoom lens. Through continuously tracing and identifying a target on the structure, the structural displacement is derived through cross-correlation analysis between the predefined pattern and the captured digital images with the aid of a pattern matching algorithm. To validate the developed system, MTS tests of sinusoidal motions under different vibration frequencies and amplitudes and shaking table tests with different excitations (the El-Centro earthquake wave and a sinusoidal motion) are carried out. Additionally, in-situ verification experiments are performed to measure the mid-span vertical displacement of the suspension Tsing Ma Bridge in the operational condition and the cable-stayed Stonecutters Bridge during loading tests. The obtained results show that the developed system exhibits an excellent capability in real-time measurement of structural displacement and can serve as a good complement to the traditional sensors.

키워드

과제정보

연구 과제 주관 기관 : The Hong Kong Polytechnic University

참고문헌

  1. Chang, C.C. and Ji, Y.F. (2007), "Flexible videogrammetric technique for three-dimensional structural vibration measurement", J. Eng. Mech. - ASCE, 133(6), 656-664. https://doi.org/10.1061/(ASCE)0733-9399(2007)133:6(656)
  2. Choi, H.S., Cheung, J.H., Kim, S.H. and Ahn, J.H. (2011), "Structural dynamic displacement vision system using digital image processing", NDT& E. Int., 44(7), 597-608. https://doi.org/10.1016/j.ndteint.2011.06.003
  3. Fukuda, Y., Feng, M.Q. and Shinozuka, M. (2010), "Cost-effective vision-based system for monitoring dynamic response of civil engineering structures", Struct. Health Monit., 17(8), 918-936. https://doi.org/10.1002/stc.360
  4. Gonzalez, R.C. and Woods, R.E. (2008), Digital image processing, 3rd Ed, Pearson Prentice Hall, Upper Saddle River, NJ.
  5. Jauregui, D.V., White, K.R., Woodward, C.B. and Leitch, K.R. (2003), "Noncontact photogrammetric measurement of vertical bridge deflection", J. Bridge Eng. - ASCE, 8(4), 212-222. https://doi.org/10.1061/(ASCE)1084-0702(2003)8:4(212)
  6. Jurjo, D.L.B.R., Magluta, C., Roitman, N. and Goncalves, P.B. (2010), "Experimental methodology for the dynamic analysis of slender structures based on digital image processing techniques", Mech. Syst. Signal Pr., 24(5), 1369-1382. https://doi.org/10.1016/j.ymssp.2009.12.006
  7. Kim, N.S. and Cho, N.S. (2004), "Estimating deflection of a simple beam model using fiber optic Bragg-grating sensors" , Exp. Mech., 44(4), 433-439. https://doi.org/10.1007/BF02428097
  8. Kim, W. and Laman, J.A. (2012), "Seven-year field monitoring of four integral abutment bridges", J. Perform. Constr. Fac. - ASCE, 26(1), 54-64. https://doi.org/10.1061/(ASCE)CF.1943-5509.0000250
  9. Ko, J.M. and Ni, Y.Q. (2005), "Technology developments in structural health monitoring of large-scale bridges", Eng. Struct., 27(12), 1715-1725. https://doi.org/10.1016/j.engstruct.2005.02.021
  10. Lee, J.J. and Shinozuka, M. (2006), "A vision-based system for remote sensing of bridge displacement", NDT& E. Int., 39(5), 425-431. https://doi.org/10.1016/j.ndteint.2005.12.003
  11. Nassif, H.H., Gindy, M. and Davis, J. (2005), "Comparison of laser Doppler vibrometer with contact sensors for monitoring bridge deflection and vibration", NDT&E. Int., 38(3), 213-218. https://doi.org/10.1016/j.ndteint.2004.06.012
  12. Ni, Y.Q., Xia, Y., Liao, W.Y. and Ko, J.M. (2009), "Technology innovation in developing the structural health monitoring system for Guangzhou New TV Tower", Struct. Health Monit., 16(1), 73-98. https://doi.org/10.1002/stc.303
  13. Okasha, N.M. and Frangopol, D.M. (2012), "Integration of structural health monitoring in a system performance based life-cycle bridge management framework", Struct. Infrastruct. E., 8(11), 999-1016.
  14. Park, K.T., Kim, S.H., Park, H.S. and Lee, K.W. (2005), "The determination of bridge displacement using measured acceleration", Eng. Struct., 27(3), 371-378. https://doi.org/10.1016/j.engstruct.2004.10.013
  15. 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. Health Monit., 19(3), 385-404. https://doi.org/10.1002/stc.438
  16. Stull, C.J., Hemez, F.M. and Farrar, C.R. (2012), "On assessing the robustness of structural health monitoring technologies", Struct. Health Monit., 11(6), 712-723. https://doi.org/10.1177/1475921712451956
  17. 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. https://doi.org/10.1088/0964-1726/12/5/016
  18. Wong, K.Y. (2004), "Instrumentation and health monitoring of cable-supported bridges", Struct. Health Monit., 11(2), 91-124. https://doi.org/10.1002/stc.33
  19. Wong, K.Y. (2007), "Design of a structural health monitoring system for long-span bridges", Struct. Infrastruct. E., 3(2), 169-185. https://doi.org/10.1080/15732470600591117
  20. Xu, L., Guo, J.J. and Jiang, J.J. (2002), "Time-frequency analysis of a suspension bridge based on GPS", J. Sound Vib., 254(1), 105-116. https://doi.org/10.1006/jsvi.2001.4087
  21. Yi, T.H., Li, H.N. and Gu, M. (2011), "Characterization and extraction of global positioning system multipath signals using improved particle filtering algorithm", Meas. Sci. Technol., 22(7), Article ID 075101: 1-11.
  22. Yi, T.H., Li, H.N. and Gu, M. (2012). "Recent research and applications of GPS-based monitoring technology for high-rise structures", Struct. Health Monit., DOI: 10.1002/stc.1501.
  23. Yi, T.H., Li, H.N. and Gu, M. (2013), "Experimental assessment of high-rate GPS receivers for deformation monitoring of bridge", Measurement, 46(1), 420-432. https://doi.org/10.1016/j.measurement.2012.07.018
  24. Zaurin, R. and Catbas, F.N. (2010), "Integration of computer imaging and sensor data for structural health monitoring of bridges", Smart Mater. Struct., 19(1), 1-15.

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  4. Deflection monitoring and assessment for a suspension bridge using a connected pipe system: a case study in China vol.22, pp.12, 2015, https://doi.org/10.1002/stc.1751
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  6. Computer vision for SHM of civil infrastructure: From dynamic response measurement to damage detection – A review vol.156, 2018, https://doi.org/10.1016/j.engstruct.2017.11.018
  7. Review of machine-vision based methodologies for displacement measurement in civil structures vol.8, pp.1, 2018, https://doi.org/10.1007/s13349-017-0261-4
  8. A Practical Monitoring System for the Structural Safety of Mega-Trusses Using Wireless Vibrating Wire Strain Gauges vol.13, pp.12, 2013, https://doi.org/10.3390/s131217346
  9. Structural Health Monitoring of Civil Infrastructure Using Optical Fiber Sensing Technology: A Comprehensive Review vol.2014, 2014, https://doi.org/10.1155/2014/652329
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  20. Identification of structural dynamic characteristics based on machine vision technology 2018, https://doi.org/10.1016/j.measurement.2017.09.043
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  27. Vision-based structural displacement measurement: System performance evaluation and influence factor analysis vol.88, 2016, https://doi.org/10.1016/j.measurement.2016.01.024
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  29. Geotechnical monitoring and analyses on the stability and health of a large cross-section railway tunnel constructed in a seismic area 2017, https://doi.org/10.1016/j.measurement.2017.10.039
  30. Computer vision and deep learning–based data anomaly detection method for structural health monitoring pp.1741-3168, 2018, https://doi.org/10.1177/1475921718757405
  31. Statistical Analysis of Stress Signals from Bridge Monitoring by FBG System vol.18, pp.2, 2018, https://doi.org/10.3390/s18020491
  32. Vision-based systems for structural deformation measurement: case studies vol.171, pp.12, 2018, https://doi.org/10.1680/jstbu.17.00134
  33. A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge vol.25, pp.5, 2018, https://doi.org/10.1002/stc.2155
  34. A parallel stereovision method used for monitoring the collapse of a three-story frame model subjected to seismic loading vol.14, pp.9, 2018, https://doi.org/10.1177/1550147718800626
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  77. Vision-Based Vibration Monitoring of Structures and Infrastructures: An Overview of Recent Applications vol.6, pp.1, 2013, https://doi.org/10.3390/infrastructures6010004
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