Development and Laboratory Testing of a Multipoint Displacement Monitoring System

  • Darragh LydonEmail author
  • Su Taylor
  • Des Robinson
  • Necati Catbas
  • Myra Lydon
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)


This paper develops a synchronized multi-camera contactless vision based multiple point displacement measurement system using wireless action cameras. Displacement measurements can provide a valuable insight into the structural condition and service behavior of bridges under live loading. Traditional means of obtaining displacement readings include displacement gauges or GPS based systems which can be limited in terms of accuracy and access. Computer Vision systems can provide a promising alternative means of displacement calculation, however existing systems in use are limited in scope by their inability to reliably track multiple points on a long span bridge structure. The system introduced in this paper provides a low-cost durable alternative which is rapidly deployable. Commercial action cameras were paired with an industrially validated solution for synchronization to provide multiple point displacement readings. The performance of the system was evaluated in a series of controlled laboratory tests. This included the development of displacement identification algorithms which were rigorously tested and validated against fiber optic displacement measurements. The results presented in this paper provide the knowledge for a step change in the application current vision based Structural health monitoring (SHM) systems which can be cost prohibitive and provides rapid method of obtaining data which accurately relates to measured bridge deflections.


Computer vision Structural health monitoring Bridge monitoring 


  1. 1.
    Romp, W., De Haan, J.: Public capital and economic growth: a critical survey. Perspektiven der Wirtschaftspolitik 8(Spec. Issue), 6–52 (2007). Scholar
  2. 2.
    Office of National Statistics: Developing new statistics of infrastructure: August 2018 (2018)Google Scholar
  3. 3.
    World Economic Forum: The global competitiveness Report 2018 - Reports - World Economic Forum (2018)Google Scholar
  4. 4.
    RAC Foundation: Council road bridge maintenance in Great Britain. Accessed 14 Mar 2018
  5. 5.
    OECD: Transport infrastructure investment and maintenance spending (2016)Google Scholar
  6. 6.
    ACSE: Report card for America’s infrastructure (2017)Google Scholar
  7. 7.
    Graybeal, B.A., Phares, B.M., Rolander, D.D., Moore, M., Washer, G.: Visual inspection of highway bridges. J. Nondestruct. Eval. 21(3), 67–83 (2002)CrossRefGoogle Scholar
  8. 8.
    See, J.E.: SANDIA REPORT Visual Inspection: A Review of the Literature (2012)Google Scholar
  9. 9.
    Lydon, D., et al.: Development and field testing of a time-synchronized system for multi-point displacement calculation using low cost wireless vision-based sensors. IEEE Sens. J., 1 (2018)Google Scholar
  10. 10.
    Lee, J.J., Shinozuka, M.: Real-time displacement measurement of a flexible bridge using digital image processing techniques. Exp. Mech. 46(1), 105–114 (2006)CrossRefGoogle Scholar
  11. 11.
    Jin, Y., Feng, M., Luo, T., Zhai, C.: A sensor for large strain deformation measurement with automated grid method based on machine vision. In: International Conference on Intelligent Robotics and Applications, pp. 417–428. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  12. 12.
    Kromanis, R., Kripakaran, P.: Predicting thermal response of bridges using regression models derived from measurement histories. Comput. Struct. 136, 64–77 (2014)CrossRefGoogle Scholar
  13. 13.
    Goncalves, P.B., Jurjo, D.L.B.R., Magluta, C., Roitman, N.: Experimental investigation of the large amplitude vibrations of a thin-walled column under self-weight. Struct. Eng. Mech. 46(6), 869–886 (2013)CrossRefGoogle Scholar
  14. 14.
    Khuc, T., Catbas, F.N.: Computer vision-based displacement and vibration monitoring without using physical target on structures. Struct. Infrastruct. Eng. 13(4), 505–516 (2017)CrossRefGoogle Scholar
  15. 15.
    Feng, D., Feng, M.Q., Ozer, E., Fukuda, Y.: A vision-based sensor for noncontact structural displacement measurement. Sensors 15(7), 16557–16575 (2015)CrossRefGoogle Scholar
  16. 16.
    Lages Martins, L.L., Rebordão, J.M., Silva Ribeiro, A.S.: Structural observation of long-span suspension bridges for safety assessment: Implementation of an optical displacement measurement system. In: Journal of Physics: Conference Series, vol. 588, no. 1, p. 12004 (2015)Google Scholar
  17. 17.
    Feng, D., Feng, M.Q.: Vision-based multipoint displacement measurement for structural health monitoring. Struct. Control Heal. Monit. 23(5), 876–890 (2016)CrossRefGoogle Scholar
  18. 18.
    Ho, H.-N., Lee, J.-H., Park, Y.-S., Lee, J.-J.: A synchronized multipoint vision-based system for displacement measurement of civil infrastructures. Sci. World J. 2012, 1–9 (2012)Google Scholar
  19. 19.
    Park, J.-W., Lee, J.-J., Jung, H.-J., Myung, H.: Vision-based displacement measurement method for high-rise building structures using partitioning approach. NDT E Int. 43(7), 642–647 (2010)CrossRefGoogle Scholar
  20. 20.
    Timecode Systems: SyncBac Pro Home | Timecode Systems (2016). Accessed 09 Mar 2018
  21. 21.
  22. 22.
    Celik, O., Terrell, T., Gul, M., Catbas, F.N.: Sensor clustering technique for practical structural monitoring and maintenance. Struct. Monit. Maint. 5(2), 273–295 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Darragh Lydon
    • 1
    Email author
  • Su Taylor
    • 1
  • Des Robinson
    • 1
  • Necati Catbas
    • 2
  • Myra Lydon
    • 1
  1. 1.School of Natural and Build EnvironmentQueens University BelfastBelfastUK
  2. 2.University of Central FloridaOrlandoUSA

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