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Multi-point displacement monitoring of bridges using a vision-based approach

  • Ye, X.W. (Department of Civil Engineering, Zhejiang University) ;
  • Yi, Ting-Hua (School of Civil Engineering, Dalian University of Technology) ;
  • Dong, C.Z. (Department of Civil Engineering, Zhejiang University) ;
  • Liu, T. (Department of Civil Engineering, Zhejiang University) ;
  • Bai, H. (Tangram Electronic Engineering Co. Ltd.)
  • Received : 2014.10.16
  • Accepted : 2014.12.25
  • Published : 2015.02.25

Abstract

To overcome the drawbacks of the traditional contact-type sensor for structural displacement measurement, the vision-based technology with the aid of the digital image processing algorithm has received increasing concerns from the community of structural health monitoring (SHM). The advanced vision-based system has been widely used to measure the structural displacement of civil engineering structures due to its overwhelming merits of non-contact, long-distance, and high-resolution. However, seldom currently-available vision-based systems are capable of realizing the synchronous structural displacement measurement for multiple points on the investigated structure. In this paper, the method for vision-based multi-point structural displacement measurement is presented. A series of moving loading experiments on a scale arch bridge model are carried out to validate the accuracy and reliability of the vision-based system for multi-point structural displacement measurement. The structural displacements of five points on the bridge deck are measured by the vision-based system and compared with those obtained by the linear variable differential transformer (LVDT). The comparative study demonstrates that the vision-based system is deemed to be an effective and reliable means for multi-point structural displacement measurement.

Keywords

structural health monitoring;dynamic displacement;vision-based system;digital image processing technique;pattern matching algorithm

Acknowledgement

Supported by : National Natural Science Foundation of China

References

  1. Adhikari, R.S., Bagchi, A. and Moselhi, O. (2014), "Automated condition assessment of concrete bridges with digital imaging", Smart Struct. Syst., 13(6), 901-925. https://doi.org/10.12989/sss.2014.13.6.901
  2. Busca, G., Cigada, A., Mazzoleni, P. and Zappa, E. (2014), "Vibration monitoring of multiple bridge points by means of a unique vision-based measuring system", Exp. Mech., 54(2), 255-271. https://doi.org/10.1007/s11340-013-9784-8
  3. Cho, S., Sim, S.H., Park, J.W. and Lee, J. (2014), "Extension of indirect displacement estimation method using acceleration and strain to various types of beam structures", Smart Struct. Syst., 14(4), 699-718. https://doi.org/10.12989/sss.2014.14.4.699
  4. Gales, J.A., Bisby, L.A. and Stratford, T. (2012), "New parameters to describe high-temperature deformation of prestressing steel determined using digital image correlation", Struct. Eng. Int., 22(4), 476-486. https://doi.org/10.2749/101686612X13363929517730
  5. German, S., Jeon, J.S., Zhu, Z.H., Bearman, C., Brilakis, I., DesRoches, R. and Lowes, L. (2013), "Machine vision-enhanced postearthquake inspection", J. Comput. Civil Eng. - ASCE, 27(6), 622-634. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000333
  6. Gonzalez, R.C. and Woods, R.E. (2008), Digital Image Processing, 3rd Ed., Pearson Prentice Hall, Upper Saddle River, NJ.
  7. Ho, H.N., Kim, K.D., Park, Y.S. and Lee, J.J. (2013), "An efficient image-based damage detection for cable surface in cable-stayed bridges", NDT&E Int., 58, 18-23. https://doi.org/10.1016/j.ndteint.2013.04.006
  8. Im, S.B., Hurlebaus, S. and Kang, Y.J. (2013), "Summary review of GPS technology for structural health monitoring", J. Struct. Eng.- ASCE, 139(10), 1653-1664. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000475
  9. 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)
  10. Jeon, H., Kim, Y., Lee, D. and Myung, H. (2014), "Vision-based remote 6-DOF structural displacement monitoring system using a unique marker", Smart Struct. Syst., 13(6), 927-942. https://doi.org/10.12989/sss.2014.13.6.927
  11. Kaito, K., Abe, M. and Fujino, Y. (2005), "Development of non-contact scanning vibration measurement system for real-scale structures", Struct. Infrastruct. E., 1(3), 189-205. https://doi.org/10.1080/15732470500030661
  12. Koch, C., Paal, S.G., Rashidi, A., Zhu, Z., Konig, M. and Brilakis, I. (2014), "Achievements and challenges in machine vision-based inspection of large concrete structures", Adv. Struct. Eng., 17(3), 303-318. https://doi.org/10.1260/1369-4332.17.3.303
  13. Kohut, P., Holak, K., Uhl, T., Ortyl, L., Owerko, T., Kuras, P. and Kocierz, R. (2013), "Monitoring of a civil structure's state based on noncontact measurements", Struct. Health Monit., 12(5-6), 411-429. https://doi.org/10.1177/1475921713487397
  14. Lee, J.H., Ho, H.N., Shinozuka, M. and Lee, J.J. (2012), "An advanced vision-based system for real-time displacement measurement of high-rise builidings", Smart Mater. Struct., 21, 1-11.
  15. 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
  16. Li, Y.L., Qiang, S.Z., Liao, H.L. and Xu, Y.L. (2006), "Dynamics of wind-rail vehicle-bridge systems", J. Wind Eng. Ind. Aerod., 93, 483-507.
  17. Li, Y.L., Hu, P., Cai, C.S. and Qiang, S.Z. (2013a), "Wind tunnel study of sudden change of vehicle wind loads due to windshield effects of bridge towers and passing vehicles", J. Eng. Mech.- ASCE, 139(9), 1249-1259 https://doi.org/10.1061/(ASCE)EM.1943-7889.0000559
  18. Li, Y.L., Xiang, H.Y., Wang, B., Xu, Y.L. and Qiang. S.Z. (2013b), "Dynamic analysis of wind-vehicle-bridge system with two trains interaction", Adv. Struct. Eng., 16(10), 1663-1670. https://doi.org/10.1260/1369-4332.16.10.1663
  19. Li, Q., Wang, S.G., Guan, B.Q. and Wang, G.B. (2007), "A machine vision method for the measurement of vibration amplitude", Meas. Sci. Technol., 18, 1477-1486. https://doi.org/10.1088/0957-0233/18/5/038
  20. Liu, Y.F., Cho, S., Spencer, B.F. and Fan, J.S. (2014), "Automated assessment of cracks on concrete surfaces using adaptive digital image processing", Smart Struct. Syst., 14(4), 719-741. https://doi.org/10.12989/sss.2014.14.4.719
  21. Mazzoleni, P. and Zappa, E. (2012), "Vision-based estimation of vertical dynamic loading induced by jumping and bobbing crowds on civil structures", Mech. Syst. Signal Pr., 33, 1-12. https://doi.org/10.1016/j.ymssp.2012.06.009
  22. McCormick, N., Owens, A. and Waterfall, P. (2014), "Optical imaging for low-cost structural measurements", Proceedings of the Institution of Civil Engineers: Bridge Engineering, 167(1), 33-42.
  23. Meng, X., Dodson, A.H. and Roberts, G.W. (2007), "Detecting bridge dynamics with GPS and triaxial accelerometers", Eng. Struct., 29(11), 3178-3184. https://doi.org/10.1016/j.engstruct.2007.03.012
  24. Moschas, F. and Stiros, S. (2011), "Measurement of the dynamic displacements and of the modal frequencies of a short-span pedestrian bridge using GPS and an accelerometer", Eng. Struct., 33(1), 10-17. https://doi.org/10.1016/j.engstruct.2010.09.013
  25. Nakamura, S.I. (2000), "GPS measurement of wind-induced suspension bridge girder displacements", J. Struct. Eng. - ASCE, 126(12), 1413-1419. https://doi.org/10.1061/(ASCE)0733-9445(2000)126:12(1413)
  26. Ni, Y.Q., Ye, X.W. and Ko, J.M. (2010), "Monitoring-based fatigue reliability assessment of steel bridges: analytical model and application", J. Struct. Eng. - ASCE, 136(12), 1563-1573. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000250
  27. Ni, Y.Q., Ye, X.W. and Ko, J.M. (2012), "Modeling of stress spectrum using long-term monitoring data and finite mixture distributions", J. Eng. Mech.- ASCE, 138(2), 175-183. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000313
  28. Park, J.W., Lee, J.J., Jung, H.J. and Myung, H. (2010), "Vision-based displacement measurement method for high-rise building structures using partitioning approach", NDT&E Int., 43(7), 642-647. https://doi.org/10.1016/j.ndteint.2010.06.009
  29. Payo, I. and Feliu, V. (2014), "Strain gauges based sensor system for measuring 3-D deflections of flexible beams", Sensor Actuat. A Phys., 217, 81-94. https://doi.org/10.1016/j.sna.2014.06.014
  30. Poudel, U.P., Fu, G. and Ye, J. (2005), "Structural damage detection using digital video imaging technique and wavelet transformation", J. Sound Vib., 286(4-5), 869-895. https://doi.org/10.1016/j.jsv.2004.10.043
  31. Ribeiro, D., Calcada, R., Ferreira, J. and Martins, T. (2014), "Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system", Eng. Struct., 75, 164-180. https://doi.org/10.1016/j.engstruct.2014.04.051
  32. 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. https://doi.org/10.1002/stc.438
  33. Wang, Y. and Cuitino, A.M. (2002), "Full-field measurements of heterogeneous deformation patterns on polymeric foams using digital image correlation", Int. J. Solids Struct., 39(13-14), 3777-3796. https://doi.org/10.1016/S0020-7683(02)00176-2
  34. Winkler, J., Fischer, G. and Georgakis, C.T. (2014), "Measurement of local deformation in steel monostrands using digital image correlation", J. Bridge Eng.- ASCE, 19(10), 1-9. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000561
  35. Wu, L.J., Casciati, F. and Casciati, S. (2014), "Dynamic testing of a laboratory model via vision-based sensing", Eng. Struct., 60, 113-125. https://doi.org/10.1016/j.engstruct.2013.12.002
  36. Yi, T.H., Li, H.N. and Gu, M. (2011), "Optimal sensor placement for structural health monitoring based on multiple optimization strategies", Struct. Des. Tall Spec. Build., 20(7), 881-900. https://doi.org/10.1002/tal.712
  37. Yi, T.H., Li, H.N. and Zhang, X.D. (2012), "A modified monkey algorithm for optimal sensor placement in structural health monitoring", Smart Mater. Struct., 21(10), 1-9.
  38. 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
  39. Ye, X.W., Ni, Y.Q., Wong, K.Y. and Ko, J.M. (2012), "Statistical analysis of stress spectra for fatigue life assessment of steel bridges with structural health monitoring data", Eng. Struct., 45, 166-176. https://doi.org/10.1016/j.engstruct.2012.06.016
  40. Ye, X.W., Ni, Y.Q., Wai, T.T., Wong, K.Y., Zhang, X.M. and Xu, F. (2013), "A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification", Smart Struct. Syst., 12(3-4), 363-379. https://doi.org/10.12989/sss.2013.12.3_4.363
  41. Ye, X.W., Su, Y.H. and Han, J.P. (2014), "Structural health monitoring of civil infrastructure using optical fiber sensing technology: A comprehensive review", Sci. World J., 2014, Article ID 652329, 1-11.
  42. 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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