Verification of Long-distance Vision-based Displacement Measurement System

장거리 영상기반 변위계측 시스템 검증

  • 김홍진 (경북대학교 건설환경에너지공학부) ;
  • 허석재 (단국대학교 건축공학과) ;
  • 신승훈 (경북대학교 건설환경에너지공학부)
  • Received : 2018.10.18
  • Accepted : 2018.11.05
  • Published : 2018.12.25

Abstract

The purpose of this study is to verify the long - range measurement performance for practical field application of VDMS. The reliability of the VDMS was verified by comparison with the existing monitoring sensor, GPS, Accelerometer and LDS. It showed the ability to accurately measure the dynamic displacement by tracking a motion of free vibration of target. And using the PSD function of measured data, the results in the frequency domain were also analyzed. We judged that VDMS is able to identify the higher system mode and has sufficient reliability. Based on the reliability verification, we conducted tests for long-distance applicability for actual application of VDMS. The distance from the stationary target model structure was increased by 50m interval, and the maximum distance was set to 400m. From the distance of 150m, the image obtained by the commercial camcorder has an error in the analysis, so the measured displacement comparison was performed between the LDS and the refractor telescope measurement results. In the measurement results of the displacement area of VDMS, the data validity was deteriorated due to the data shift by the external force and the quality degradation of the enlarged image. However, even under the condition that the effectiveness of the displacement measurement data of VDMS is low, the first mode characteristic included in the free vibration of the object is clearly measured. If the influence from the external environment is controlled and stable data is collected, It is judged that reliability of long-distance VDMS can be secured.

Keywords

Acknowledgement

Supported by : 국립재난안전연구원

References

  1. Khuc, T., Catbas, F.N. (2017). Computer vision-based displacement and vibration monitoring without using physical target on structures. Structure and Infrastructure Engineering, 13(4), pp.505-516. https://doi.org/10.1080/15732479.2016.1164729
  2. Heo, S.J., Park, H.H., Mun, D.H., Lee, S.H. (2017). System identification of a 5 story steel frame under ambient wind loads using RTK-GPS. Journal of the Wind Engineering Institute of Korea, 21(1), pp.29-35.
  3. Kohut, P., Holak, K., Uhl, T., Ortyl, L., Owerko, T., Kuras, P., Kocierz, R. (2013). Monitoring of a civil structure's state based on noncontact measurements. Structural Health Monitoring, 12(5-6), pp.411-429. https://doi.org/10.1177/1475921713487397
  4. Fukuda, Y., Feng, M.Q., Shinozuka, M. (2010). Costeffective vision‐based system for monitoring dynamic response of civil engineering structures. Structural Control and Health Monitoring, 17(8), pp.918-936. https://doi.org/10.1002/stc.360
  5. Vest, C.M. (2008). Context and Challenge for Twenty‐First Century Engineering Education. Journal of Engineering education, 97(3), pp.235-236. https://doi.org/10.1002/j.2168-9830.2008.tb00973.x
  6. Fraser, C.S., Riedel, B. (2000). Monitoring the thermal deformation of steel beams via vision metrology. ISPRS Journal of Photogrammetry and Remote Sensing, 55(4), pp.268-276. https://doi.org/10.1016/S0924-2716(00)00024-1
  7. White, D.J., Take, W.A., Bolton, M.D. (2003). Soil deformation measurement using particle image velocimetry (PIV) and photogrammetry. Geotechnique, 53(7), pp.619-631. https://doi.org/10.1680/geot.2003.53.7.619
  8. Park, J.C., Cho, J.S., Gil, H.B., Shin, J.I. (2014). Vision-Based Technology for Structural Health Monitoring of Bridge. Magazine of the Korea Institute for Structural Maintenance and Inspection, 18(2), pp.10-16.
  9. Fukuda, Y., Feng, M.Q., Narita, Y., Kaneko, S.I., Tanaka, T. (2013). Vision-based displacement sensor for monitoring dynamic response using robust object search algorithm. IEEE Sensors Journal, 13(12), pp.4725-4732. https://doi.org/10.1109/JSEN.2013.2273309
  10. Lee, J.J., Shinozuka, M. (2006). A vision-based system for remote sensing of bridge displacement. Ndt & E International, 39(5), pp.425-431. https://doi.org/10.1016/j.ndteint.2005.12.003
  11. Kim, S.W., Kim, N.S. (2009). Multi-point dynamic displacement measurements of structures using digital image correlation technique. Journal of the Earthquake Engineering Society of Korea, 13(3), pp.11-19.
  12. Lee, J.H., Ho, H.N., Shinozuka, M., Lee, J.J. (2012). An advanced vision-based system for real-time displacement measurement of high-rise buildings. Smart Materials and Structures, 21(12), 125019. https://doi.org/10.1088/0964-1726/21/12/125019
  13. Choi, H.S., Cheung, J.H., Kim, S.H., Ahn, J.H. (2011). Structural dynamic displacement vision system using digital image processing. NDT & E International, 44(7), pp.597-608. https://doi.org/10.1016/j.ndteint.2011.06.003
  14. Chen, S., Li, X., Zhang, H., Chen, G., Liu, A., Du, X. (2015). Vision-based displacement test method for high-rise building shaking table test. Journal of Vibroengineering, 17(8), pp.4057-4068.
  15. Shin, S.H., Shin, K.J., Kim, H.J. (2017). Reliability verification of vision-based dynamic displacement measurement system. Journal of the Wind Engineering Institute of Korea, 21(3), pp.135-142.
  16. Shin, S.H., Park, W.B., Kim, H.J. (2017). Vision-based system identification for MDOF structures through shaking table test. Journal of the Wind Engineering Institute of Korea, 21(4), pp.179-186.
  17. Shin, S.H., Park, W.B., Seol, D.H., Kim, H.J. (2018). Comparison of vision-based displacement measurement results for various target shooting angles. Journal of the Wind Engineering Institute of Korea, 22(3), pp.103-109.
  18. ASCE. (2017). Minimum design loads and associated criteria for buildings and other structures (7-16), ASCE/SEI
  19. https://www.sony.co.kr/electronics/handycam-camcorders/fdr-axp55/specifications
  20. https://www.sony.co.kr/electronics/interchangeable-lens-cameras/ilce-6500-body-kit/specifications
  21. https://www.costco.ca/Celestron-NexStar-90GT-Computerized-Telescope.product.100229203.html
  22. https://kinemetrics.com/post_products/episensor-es-u2
  23. https://hds.leica-geosystems.com/en/Leica-GR10_83449.htm
  24. http://ekais.kr/ms-laser-displacement-sensor-kl3