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


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.


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


Supported by : National Natural Science Foundation of China


  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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
  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.
  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.
  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.
  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.
  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.
  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.
  25. Nakamura, S.I. (2000), "GPS measurement of wind-induced suspension bridge girder displacements", J. Struct. Eng. - ASCE, 126(12), 1413-1419.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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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