Smart Structures and Systems

  • 제25권5호
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  • Pages.531-541
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  • 2020
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Post-earthquake building safety evaluation using consumer-grade surveillance cameras

  • Hsu, Ting Y. (Department of Civil and Construction Engineering, National Taiwan University of Science and Technology) ;
  • Pham, Quang V. (Taiwan Building Technology Center, National Taiwan University of Science and Technology) ;
  • Chao, Wei C. (Taiwan Department of Civil Engineering, National Taipei University of Technology) ;
  • Yang, Yuan S. (Taiwan Department of Civil Engineering, National Taipei University of Technology)
  • 투고 : 2019.05.28
  • 심사 : 2019.12.27
  • 발행 : 2020.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper demonstrates the possibility of evaluating the safety of a building right after an earthquake using consumer-grade surveillance cameras installed in the building. Two cameras are used in each story to extract the time history of interstory drift during the earthquake based on camera calibration, stereo triangulation, and image template matching techniques. The interstory drift of several markers on the rigid floor are used to estimate the motion of the geometric center using the least square approach, then the horizontal interstory drift of any location on the floor can be estimated. A shaking table collapse test of a steel building was conducted to verify the proposed approach. The results indicate that the accuracy of the interstory drift measured by the cameras is high enough to estimate the damage state of the building based on the fragility curve of the interstory drift ratio. On the other hand, the interstory drift measured by an accelerometer tends to underestimate the damage state when residual interstory drift occurs because the low frequency content of the displacement signal is eliminated when high-pass filtering is employed for baseline correction.

키워드

과제정보

The authors are grateful to the financial support from the Ministry of Science and Technology of Republic of China under Grant MOST106-2622-M-011-002-CC2. This work was also financially supported by the Taiwan Building Technology Center from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education in Taiwan.

참고문헌

  1. Bouguet, J.Y. (2014), Camera Calibration Toolbox for Matlab. Software available at web address: http://www.vision.caltech.edu/bouguetj/calib_doc/
  2. Bradski, G. and Kaehler, A. (2008), Learning OpenCV Computer Vision with the OpenCV Library, O'Reilly Media,WI, USA.
  3. Federal Emergency Management Agency (2004), HAZUS-MH Technical Manual, Washington, D.C., USA.
  4. Harvey Jr, P.S. and Elisha, G. (2018), "Vision-based vibration monitoring using existing cameras installed within a building", Struct. Control Health Monit., 25(11), e2235. https://doi.org/10.1002/stc.2235
  5. Khuc, T. and Catbas, F.N. (2017), "Computer vision-based displacement and vibration monitoring without using physical target on structures", Struct. Infrastruct. Eng., 13, 505-516. https://doi.org/10.1080/15732479.2016.1164729
  6. Lee, J., Lee, K.C., Cho, S. and Sim, S.H. (2017), "Computer vision-based structural displacement measurement robust to light-induced image degradation for in-service bridges", Sensors, 17, 2317. https://doi.org/10.3390/s17102317
  7. Naeim, F., Hagie, S., Alimoradi, A. and Miranda, E. (2006), "Automated post-earthquake damage assessment of instrumented buildings", In: Advances in Earthquake Engineering for Urban Risk Reduction, ed: Springer, pp. 117-134.
  8. Park, J.W., Sim, S.H. and Jung, H.J. (2013), "Displacement estimation using multimetric data fusion", IEEE/ASME Transact. Mechatron., 18, 1675-1682. https://doi.org/10.1109/TMECH.2013.2275187
  9. Skolnik, D.A. and Wallace, J.W. (2010), "Critical assessment of interstory drift measurements", J. Struct. Eng., 136, 1574-1584. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000255
  10. Trapani, D., Maroni, A., Debiasi, E. and Zonta, D. (2015), "Uncertainty evaluation of after-earthquake damage detection strategy", Proceedings of 2015 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, EESMS 2015, pp. 125-130.
  11. Yang, Y.S., Huang, C.W. and Wu, C.L. (2012), "A simple image-based strain measurement method for measuring the strain fields in an RC-wall experiment", Earthq. Eng. Struct. Dyn., 41(1), 1-17. https://doi.org/10.1002/eqe.1111
  12. Yang, Y.S., Yang, C.M. and Huang, C.W. (2015), "Thin crack observation in a reinforced concrete bridge pier test using image processing and analysis", Adv. Eng. Software, 83, 99-108. https://doi.org/10.1016/j.advengsoft.2015.02.005
  13. Yang, Y.S., Wu, C.L., Hsu, T.T.C., Yang, H.C. and Lu, H.J. (2018), "Image analysis method for crack distribution and width estimation for reinforced concrete structures", Automat. Constr., 91, 120-132. https://doi.org/10.1016/j.autcon.2018.03.012
  14. Yazgan, U. and Dazio, A. (2012), "Post-earthquake damage assessment using residual displacements", Earthq. Eng. Struct. Dyn., 41, 1257-1276. https://doi.org/10.1002/eqe.1184
  15. Yoon, H., Elanwar, H., Choi, H., Golparvar-Fard, M. and Spencer Jr, B.F. (2016), "Target-free approach for vision-based structural system identification using consumer-grade cameras", Struct. Control Health Monitor., 23, 1405-1416. https://doi.org/10.1002/stc.1850

피인용 문헌

  1. A Stand-Alone Smart Camera System for Online Post-Earthquake Building Safety Assessment vol.20, pp.12, 2020, https://doi.org/10.3390/s20123374