Skip to main content
Log in

Seismic Response Measurement of an Under-Water Model Through High Speed Camera and Feature Tracking

  • Published:
Experimental Techniques Aims and scope Submit manuscript

Abstract

Shake table testing is a viable validation tool for bench-marking the analytically computed seismic response of under-water structural models with fluid–structure interaction effects. Conventional displacement sensors like LVDTs (linearly variable differential transformer) cannot be used for under-water response measurements as they require a stationary platform near the measurement point as a reference. This rather limiting application of LVDTs in several situations warrants the use of high speed camera as a potential measurement tool. This article presents the vision based displacement measurement of an under-water model tested on a shake table. High speed video camera is used to record the motion during shaking. The video images are processed using a special motion tracking algorithm and displacements are measured. The sloshing effect of water on the test model and their free vibration at the end of the shaking are studied to calculate natural frequency and damping. Vital information on the allowable under-water motion of the test model is obtained and discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Reynolds, P., Dynamic Testing and Monitoring of Civil Engineering Structures, Experimental Techniques 32(6):54–57 (2008).

    Article  Google Scholar 

  2. Atamturktur, S., Pavic, A., Reynolds, P., and Boothby, T., Full-scale Modal Testing of Vaulted Gothic Churches: Lessons Learned, Experimental Techniques 33(4):65–74 (2009).

    Article  Google Scholar 

  3. Rama Rao, G.V., Sreekala, R., Gopalakrishnan, N., Sathish Kumar, K., Muthumani, K., and Lakshmanan, N., Coasting Down Signal Analysis as a Tool for Detection of Proximity of Resonances and a Case Study, Engineering Failure Analysis 18:340–353 (2011).

    Article  Google Scholar 

  4. Lakshmanan, N., Gopalakrishnan, N., Rama Rao, G.V., and Sathish Kumar, K., Dynamic Stiffness Based Computation of Response for Framed Machine Foundations, Geomechanics and Engineering - An International Journal 1(2):121–142 (2009).

    Article  Google Scholar 

  5. Lakshmanan, N., Raghu Prasad, B.K., Gopalakrishnan, N., Sreekala, R., and Rama Rao, G.V., Comparative Study on Damage Identification from Iso-eigen-value-change Contours and Smeared Damage Model, Structural Engineering and Mechanics - An International Journal 35(6):735–758 (2010).

    Article  Google Scholar 

  6. Caetano, E., Silva, S., and Bateira, J., A Vision System for Vibration Monitoring of Civil Engineering Structures, Experimental Techniques 35(4):74–82 (2011).

    Article  Google Scholar 

  7. Ji, Y.F., and Chang, C.C., Non-target Image-based Technique for Small Cable Vibration Measurement, Journal of Bridge Engineering: ASCE 13:34–42 (2008).

    Article  Google Scholar 

  8. Lee, J.J., and Shinozuka, M., A Vision-based System for Remote Sensing of Bridge Displacement, NDT & E International 39:425–431 (2006).

    Article  Google Scholar 

  9. Valenca, J., Ju, E.N.B.S., and Arau, H.J., Applications of Photogrammetry to Structural Assessment, Experimental Techniques 35(2):1–11 (2011).

    Article  Google Scholar 

  10. Chang, C.C., and Xia, X.H., Three-Dimensional Structural Translation and Rotation Measurement Using Monocular Videogrammetry, Journal of Engineering Mechanics 136:840–848 (2010).

    Article  Google Scholar 

  11. Chu, T., Mahajan, A., and Liu, C.T., An Economical Vision-Based Method to Obtain Whole-Field Deformation Profiles, Experimental Techniques 26:25–28 (2002).

    Article  Google Scholar 

  12. Patsias, S., and Staszewski, W.J., Damage Detection Using Optical Measurements and Wavelets, Structural Health Monitoring 1:0005–0022 (2002).

    Article  Google Scholar 

  13. Lee, J.J., Fukuda, Y., Shinozuka, M., Cho, S., and Yun, C.B., Development And Application of a Vision-based Displacement Measurement System For Structural Health Monitoring of Civil Structures, Smart Structures and Systems 3:373–384 (2007).

    Article  Google Scholar 

  14. Wu, P., Stanford, B., Bowman, W., Schwartz, A., and Ifju, P., Digital Image Correlation Techniques for Full-field Displacement Measurements of Micro Air Vehicle Flapping Wings, Experimental Techniques 33:53–58 (2009).

    Article  Google Scholar 

  15. Morlier, J., and Michon, G., Virtual Vibration Measurement Using KLT Motion Tracking Algorithm, Journal of Dynamic Systems, Measurement, and Control 132:200–207 (2010).

    Article  Google Scholar 

  16. Lucas, B.D., and Kanade, T., An Iterative Image Registration Technique with an Application to Stereo Vision, Proceedings of the Imaging Understanding Workshop :121–130 (1981).

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Gopalakrishnan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rao, G.V.R., Sreekala, R., Kumar, K.S. et al. Seismic Response Measurement of an Under-Water Model Through High Speed Camera and Feature Tracking. Exp Tech 40, 83–90 (2016). https://doi.org/10.1007/s40799-016-0013-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40799-016-0013-0

Keywords

Navigation