Smart Structures and Systems

  • 제24권6호
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  • Pages.709-721
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  • 2019
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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

Vision-based dense displacement and strain estimation of miter gates with the performance evaluation using physics-based graphics models

  • Narazaki, Yasutaka (Department of Civi and Environmentall Engineering, University of Illinois at Urbana-Champaign) ;
  • Hoskere, Vedhus (Department of Civi and Environmentall Engineering, University of Illinois at Urbana-Champaign) ;
  • Eick, Brian A. (Department of Civi and Environmentall Engineering, University of Illinois at Urbana-Champaign) ;
  • Smith, Matthew D. (Army Engineer Research and Development Center) ;
  • Spencer, Billie F. (Department of Civi and Environmentall Engineering, University of Illinois at Urbana-Champaign)
  • 투고 : 2019.07.02
  • 심사 : 2019.08.29
  • 발행 : 2019.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

This paper investigates the framework of vision-based dense displacement and strain measurement of miter gates with the approach for the quantitative evaluation of the expected performance. The proposed framework consists of the following steps: (i) Estimation of 3D displacement and strain from images before and after deformation (water-fill event), (ii) evaluation of the expected performance of the measurement, and (iii) selection of measurement setting with the highest expected accuracy. The framework first estimates the full-field optical flow between the images before and after water-fill event, and project the flow to the finite element (FE) model to estimate the 3D displacement and strain. Then, the expected displacement/strain estimation accuracy is evaluated at each node/element of the FE model. Finally, methods and measurement settings with the highest expected accuracy are selected to achieve the best results from the field measurement. A physics-based graphics model (PBGM) of miter gates of the Greenup Lock and Dam with the updated texturing step is used to simulate the vision-based measurements in a photo-realistic environment and evaluate the expected performance of different measurement plans (camera properties, camera placement, post-processing algorithms). The framework investigated in this paper can be used to analyze and optimize the performance of the measurement with different camera placement and post-processing steps prior to the field test.

키워드

참고문헌

  1. Abaqus Scripting User's Guide, http://130.149.89.49:2080/v6.14/books/cmd/default.htm?startat=pt05ch10s11.html
  2. Abaqus Unified FEA - SIMULIATM by Dassault Systemes(R), https://www.3ds.com/products-ervices/simulia/products/abaqus/
  3. American Society of Civil Engineers (2017), "ASCE's 2017 Infrastructure Report Card: Structurally Deficient Bridges".
  4. Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J. and Szeliski, R. (2011), "A database and evaluation methodology for optical flow", Int. J. Comp. Vision, 92(1), 1-31. DOI:10.1007/s11263-010-0390-2.
  5. Baker, S. and Matthews, I. (2004), "Lucas-kanade 20 years on: a unifying framework", Int. J. Comp. Vision. Kluwer Academic Publishers, 56(3), 221-255. DOI:10.1023/B:VISI.0000011205.11775.fd.
  6. Barron, J.L., Fleet, D.J. and Beauchemin, S.S. (1994), "Performance of optical flow techniques", Int. J. Comp. Vision, 12(1), 43-77. DOI: 10.1007/BF01420984.
  7. Blender/Python Documentation, https://docs.blender.org/api/2.79/
  8. Blender, https://www.blender.org/
  9. Bruhn, A., Weickert, J. and Schnorr, C. (2005), "Lucas/kanade meets horn/schunck: combining local and global optic flow methods", Int. J. Comp Vision, 61(3), 211-231. DOI:10.1023/B:VISI.0000045324.43199.43.
  10. Correlated Solutions - VIC-2D, http://correlatedsolutions.com/vic-2d/
  11. Eick, B.A., Treece, Z.R., Spencer, B.F., Smith, M.D., Sweeney, S.C., Alexander, Q.G. and Foltz, S.D. (2018), "Automated damage detection in miter gates of navigation locks", Struct. Control Health., 25(1), e2053. DOI: 10.1002/stc.2053.
  12. Fischler, M.A. and Bolles, R.C. (1981), "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography", Commun. ACM., 24(6), 381-395. DOI: 10.1145/358669.358692.
  13. Fukuda, Y., Feng, M.Q., Narita, Y., Kaneko, S. and Tanaka, T. (2013), "Vision-based displacement sensor for monitoring dynamic response using robust object search algorithm", Sensors, 13(12), 4725-4732. DOI:10.1109/JSEN.2013.2273309.
  14. Ghorbani, R., Matta, F. and Sutton, M. (2015), "Full-field deformation measurement and crack mapping on confined masonry walls using digital image correlation", Exp. Mech., 55(1), 227-243. DOI: 10.1007/s11340-014-9906-y.
  15. GOM Correlate GOM, https://www.gom.com/3d-software/gomcorrelate.html.
  16. Greimann, L., Stecker, J. and Rens, K. (1992), "Inspection and rating of miter lock gates", J. Perform. Const. Fac., 5(4), 226-238. DOI: 10.1061J(ASCE)0887-3828(1991)5:4(226). https://doi.org/10.1061/(ASCE)0887-3828(1991)5:4(226)
  17. Horn, B.K.P. and Schunck, B.G. (1981), "Determining optical flow", Artif. Intell., 17(1-3), 185-203. DOI: 10.1016/0004-3702(81)90024-2.
  18. Hoskere, V., Park, J., Yoon, H. and Spencer, B.F. (2018), "Visionbased modal survey of civil infrastructure using unmanned aerial vehicles", J. Struct. Eng., 145(7), 04019062. DOI:10.1061/(ASCE)ST.1943-541X.0002321.
  19. Hoskere, V., Eick, B.A., Spencer, B.F., Smith, M.D. and Foltz, S.D. (2019), "Deep bayesian neural networks for damage quantification in miter gates of navigation locks", Struct. Control Health.
  20. Hoskere, V., Narazaki, Y. and Spencer, B.F. (2019), "Learning to detect important visual changes for structural inspections using physics-based graphics models", Int. Conf. Struct. Control Hlth. Intell. Infra. St. Louis, USA, August.
  21. Hoult, N.A., Andy T.W., Lee, C. and Dutton, M. (2013), "Experimental accuracy of two dimensional strain measurements using Digital Image Correlation", Eng. Struct., 46, 718-726. DOI: 10.1016/J.ENGSTRUCT.2012.08.018.
  22. Liu, C. (2009), "Beyond pixels: exploring new representations and applications for motion analysis", Doctoral Thesis. Massachusetts Institute of Technology, Cambridge.
  23. Lucas, B.D. and Kanade, T. (1981), "An iterative image registration technique with an application in stereo vision", Int. Joint Conf. on Artif. Intell., Vancouver, August.
  24. Mahal, M. Blanksvard, T., Taljsten, B. and Sas, G. (2015), "Using digital image correlation to evaluate fatigue behavior of strengthened reinforced concrete beams", Eng. Struct., 105, 277-288. DOI: 10.1016/j.engstruct.2015.10.017.
  25. McCormick, N. and Lord, J. (2012), "Digital image correlation for structural measurements", Civil Eng., 165(4), 185-190.
  26. Narazaki, Y., Hoskere, V., Spencer, B.F., Foltz, S.D. and Smith, M.D. (2019), "Dense 3D displacement and strain measurement framework of miter gates using computer vision", Struct. Health. Monit., Stanford, CA, USA, September.
  27. Ncorr - Open source 2D digital image correlation MATLAB software,: http://www.ncorr.com/index.php
  28. Pan, B. (2009), "Reliability-guided digital image correlation for image deformation measurement", Appl. Optics, 48(8), 1535. DOI: 10.1364/AO.48.001535.
  29. Pan, B., Qian, K., Xie, H. and Asundi, A. (2009), "Twodimensional digital image correlation for in-plane displacement and strain measurement: a review", Meas. Sc. Tech., 20(6), 62001. DOI: 10.1088/0957-0233/20/6/062001.
  30. Pan, B., Tian, L. and Song, X. (2016), "Real-time, non-contact and targetless measurement of vertical deflection of bridges using off-axis digital image correlation", NDT. E. Int., 79, 73-80. DOI: 10.1016/j.ndteint.2015.12.006.
  31. SMART Gate, https://www.erdc.usace.army.mil/Media/Fact-Sheets/Fact-Sheet-Article-View/Article/476668/smart-gate/
  32. Spencer, B. F., Hoskere, V. and Narazaki, Y. (2019), "Advances in computer vision-based civil infrastructure inspection and monitoring", Eng., 5(2), 199-222. DOI:10.1016/J.ENG.2018.11.030.
  33. Sutton, M.A., Orteu, J.J. and Schreier, H.W. (2009), "Image correlation for shape, motion and deformation measurements:basic concepts, theory and applications", Springer Science Business Media.
  34. Szeliski, R. (2011), "Computer vision: algorithms and applications", Springer, New York, NY, USA.
  35. US Army Corps of Engineers (2014), "Lock characteristics general report", Vicksburg, MI, USA.
  36. Wadhwa, N., Rubinstein, M. Durand, F. And Freeman, W.T. (2013), "Phase-based video motion processing", Trans. Graph., 32(4), 1. DOI: 10.1145/2461912.2461966.
  37. 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. Sys., 12(4), 363-379. https://doi.org/10.12989/sss.2013.12.3_4.363.
  38. Yoneyama, S., Kitagawa, A., Iwata, S., Tani, K. and Kikuta, H. (2007), "Bridge deflection measurement using digital image correlation", Exp. Tech., 31(1), 34-40. DOI: 10.1111/j.1747-1567.2006.00132.x.

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