Vision-Based Measurements to Quantify Bridge Deformations
Publication: Journal of Bridge Engineering
Volume 28, Issue 1
Abstract
Many factors are considered when inspecting and evaluating the overall condition of a bridge. Of particular consideration here is load testing of bridges to evaluate the existing load-carrying capacity. Sensor systems are often mounted directly to girders for this assessment; however, installing sensors and data acquisition systems can be an expensive and time-consuming process, particularly given that load testing does not warrant long-term monitoring. As an alternative, noncontact remote sensing techniques have been developed for measuring structural deformations, and have the potential to be used for static load test applications. These approaches do not require sophisticated instrumentation installations, and can provide a denser array of measurements, compared with conventional sensors. A particular focus has been on techniques that use video recordings, tracking the motion between subsequent video frames via computer vision methods. There are now commercial offerings for such measurement systems, as well as an array of techniques that can be used for custom applications. While such methods have seen significant testing under laboratory conditions, there are only a limited number of studies that provide comparative methodological analyses under full-scale field conditions. This paper presents a case study on the use of vision-based methods for bridge load testing, and provides a comparison of a digital image correlation (DIC) approach with a phase-based optical flow method. Two sets of field experiments were performed on bridges in the state of Delaware. The results show that vision-based methods can provide comparable results to conventional sensor installations, given sufficient consideration of the unique technical demands of these methods, as well as operational logistics. In most cases, the DIC and phase-based methods provided comparable results, though the DIC system yielded generally better accuracy, owing to a combination of algorithmic differences and additional signal postprocessing.
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Acknowledgments
This study was funded by the US Department of Transportation (USDOT) Region 3 University Transportation Center, The Center for Integrated Asset Management for Multimodal Transportation Infrastructure Systems (CIAMTIS). The authors thank Gary Wenczel (laboratory manager, University of Delaware) for his efforts in coordinating and running the experiments. The authors also express gratitude to the DelDOT for support during the load tests. Any opinions, findings, conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect those of the funding agencies, Imetrum, or DelDOT.
References
AASHTO. 2016. Manual for bridge evaluation 2nd edition, 2016 interim revisions. Washington, DC: AASHTO.
AASHTO. 2018. Manual for bridge evaluation 3rd edition, 2018 interim revisions. Washington, DC: AASHTO.
Al-Khateeb, H. T., H. W. Shenton, M. J. Chajes, and C. Aloupis. 2019. “Structural health monitoring of a cable-stayed bridge using regularly conducted diagnostic load tests.” Front. Built Environ. 5: 41. https://doi.org/10.3389/fbuil.2019.00041.
Alipour, M., S. J. Washlesky, and D. K. Harris. 2019. “Field deployment and laboratory evaluation of 2D digital image correlation for deflection sensing in complex environments.” J. Bridge Eng. 24 (4): 04019010. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001363.
Bell, E. S., P. J. Lefebvre, M. Sanayei, B. Brenner, J. D. Sipple, and J. Peddle. 2013. “Objective load rating of a steel-girder bridge using structural modeling and health monitoring.” J. Struct. Eng. 139 (10): 1771–1779. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000599.
Brownjohn, J. M. W., Y. Xu, and D. Hester. 2017. “Vision-based bridge deformation monitoring.” Front. Built Environ. 3: 23. https://doi.org/10.3389/fbuil.2017.00023.
Cha, Y. J., J. G. Chen, and O. Büyüköztürk. 2017. “Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters.” Eng. Struct. 132: 300–313. https://doi.org/10.1016/j.engstruct.2016.11.038.
Chen, J. G., T. M. Adams, H. Sun, E. S. Bell, and O. Büyüköztürk. 2018. “Camera-based vibration measurement of the world war I memorial bridge in Portsmouth, New Hampshire.” J. Struct. Eng. 144 (11): 04018207. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002203.
Chen, J. G., A. Davis, N. Wadhwa, F. Durand, W. T. Freeman, and O. Büyüköztürk. 2017. “Video camera–based vibration measurement for civil infrastructure applications.” J. Infrastruct. Syst. 23 (3): B4016013. https://doi.org/10.1061/(ASCE)IS.1943-555X.0000348.
Chen, J. G., N. Wadhwa, Y. -J. Cha, F. Durand, W. T. Freeman, and O. Buyukozturk. 2015. “Modal identification of simple structures with high-speed video using motion magnification.” J. Sound Vib. 345: 58–71. https://doi.org/10.1016/j.jsv.2015.01.024.
Das, S., and P. Saha. 2018. “A review of some advanced sensors used for health diagnosis of civil engineering structures.” Measurement 129: 68–90. https://doi.org/10.1016/j.measurement.2018.07.008.
Dong, C.-Z., O. Celik, F. N. Catbas, E. J. O’Brien, and S. Taylor. 2020. “Structural displacement monitoring using deep learning-based full field optical flow methods.” Struct. Infrastruct. Eng. 16 (1): 51–71. https://doi.org/10.1080/15732479.2019.1650078.
Feng, D., and M. Q. Feng. 2017. “Experimental validation of cost-effective vision-based structural health monitoring.” Mech. Syst. Sig. Process. 88: 199–211. https://doi.org/10.1016/j.ymssp.2016.11.021.
Fleet, D. J., and A. D. Jepson. 1990. “Computation of component image velocity from local phase information.” Int. J. Comput. Vision 5 (1): 77–104. https://doi.org/10.1007/BF00056772.
Gautama, T., and M. Van Hulle. 2002. “A phase-based approach to the estimation of the optical flow field using spatial filtering.” IEEE Trans. Neural Networks 13 (5): 1127–1136. https://doi.org/10.1109/TNN.2002.1031944.
Harmanci, Y. E., U. Gülan, M. Zimmermann, M. Holzner, and E. Chatzi. 2017. “High spatial density vibrational measurements via 3D-particle tracking velocimetry.” In Proc., 4th Conf. on Smart Monitoring, Assessment and Rehabilitation of Civil Structures. Dübendorf, Switzerland: Empa.
Hartley, R., and A. Zisserman. 2003. Multiple view geometry in computer vision. Cambridge: Cambridge University Press.
Imetrum-Website. n.d. “Applications.” Imetrum. Accessed June 25, 2021. https://www.imetrum.com/applications/.
Khaloo, A., and D. Lattanzi. 2017. “Pixel-wise structural motion tracking from rectified repurposed videos: Pixel-wise structural motion tracking from rectified repurposed videos.” Struct. Control Health Monit. 24 (11): e2009. https://doi.org/10.1002/stc.v24.11.
Lee, J. J., and M. Shinozuka. 2006. “A vision-based system for remote sensing of bridge displacement.” NDT & E Int. 39 (5): 425–431. https://doi.org/10.1016/j.ndteint.2005.12.003.
Liu, C., Y. Yuan, and M. Zhang. 2016. “Uncertainty analysis of displacement measurement with Imetrum Video Gauge.” ISA Trans. 65: 547–555. https://doi.org/10.1016/j.isatra.2016.08.020.
Luo, L., M. Q. Feng, and Z. Y. Wu. 2018. “Robust vision sensor for multi-point displacement monitoring of bridges in the field.” Eng. Struct. 163: 255–266. https://doi.org/10.1016/j.engstruct.2018.02.014.
Moore, M., B. M. Phares, B. Graybeal, D. Rolander, G. Washer, and J. Wiss. 2001. Reliability of visual inspection for highway bridges, volume I. Rep. No. McLean, VA: Turner-Fairbank Highway Research Center.
Pan, B., L. Tian, and X. Song. 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. https://doi.org/10.1016/j.ndteint.2015.12.006.
Patsias, S., and W. J. Staszewskiy. 2002. “Damage detection using optical measurements and wavelets.” Struct. Health Monit. 1 (1): 5–22. https://doi.org/10.1177/147592170200100102.
Peddle, J., A. Goudreau, E. Carlson, and E. Santini-Bell. 2011. “Bridge displacement measurement through digital image correlation.” Bridge Struct. 7 (4): 165–173. https://doi.org/10.3233/BRS-2011-031.
Poozesh, P., A. Sarrafi, Z. Mao, P. Avitabile, and C. Niezrecki. 2017. “Feasibility of extracting operating shapes using phase-based motion magnification technique and stereo-photogrammetry.” J. Sound Vib. 407: 350–366. https://doi.org/10.1016/j.jsv.2017.06.003.
Shang, Z., and Z. Shen. 2018. “Multi-point vibration measurement and mode magnification of civil structures using video-based motion processing.” Autom. Constr. 93: 231–240. https://doi.org/10.1016/j.autcon.2018.05.025.
Shenton III, H. W., M. J. Chajes, and J. Huang. 2007. Load rating of bridges without plans. Rep. No. DCT 195. Dover, DE: Delaware Center for Transportation.
Sony, S., S. Laventure, and A. Sadhu. 2019. “A literature review of next-generation smart sensing technology in structural health monitoring.” Struct. Control Health Monit. 26 (3): e2321. https://doi.org/10.1002/stc.2321.
Sun, D., S. Roth, and M. J. Black. 2010. “Secrets of optical flow estimation and their principles.” In Proc., 2010 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2432–2439. Washington, DC: IEEE Computer Society.
Sutton, M. A., J. J. Orteu, and H. Schreier. 2009. Image correlation for shape, motion and deformation measurements: Basic concepts, theory and applications. Berlin: Springer Science & Business Media.
Wadhwa, N., M. Rubinstein, F. Durand, and W. T. Freeman. 2013. “Phase-based video motion processing.” ACM Trans. Graphics 32 (4): 1–10. https://doi.org/10.1145/2461912.2461966.
Wahbeh, A. M., J. P. Caffrey, and S. F. Masri. 2003. “A vision-based approach for the direct measurement of displacements in vibrating systems.” Smart Mater. Struct. 12 (5): 785–794. https://doi.org/10.1088/0964-1726/12/5/016.
Xu, Y., and J. M. W. Brownjohn. 2018. “Review of machine-vision based methodologies for displacement measurement in civil structures.” J. Civ. Struct. Health Monit. 8 (1): 91–110. https://doi.org/10.1007/s13349-017-0261-4.
Yang, Y., C. Dorn, T. Mancini, Z. Talken, G. Kenyon, C. Farrar, and D. Mascareñas. 2017. “Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification.” Mech. Syst. Sig. Process. 85: 567–590. https://doi.org/10.1016/j.ymssp.2016.08.041.
Ye, X. W., C. Z. Dong, and T. Liu. 2016. “A review of machine vision-based structural health monitoring: Methodologies and applications.” J. Sens. 2016: 1–10. https://doi.org/10.1155/2016/7103039.
Yoneyama, S., A. Kitagawa, S. Iwata, K. Tani, and H. Kikuta. 2007. “Bridge deflection measurement using digital image correlation.” Exp. Tech. 31 (1): 34–40. https://doi.org/10.1111/j.1747-1567.2006.00132.x.
Yoneyama, S., and H. Ueda. 2012. “Bridge deflection measurement using digital image correlation with camera movement correction.” Mater. Trans. 53 (2): 285–290. https://doi.org/10.2320/matertrans.I-M2011843.
Zhang, Z. 2000. “A flexible new technique for camera calibration.” IEEE Trans. Pattern Anal. Mach. Intell. 22 (11): 1330–1334. https://doi.org/10.1109/34.888718.
Information & Authors
Information
Published In
Journal of Bridge Engineering
Volume 28 • Issue 1 • January 2023
Copyright
© 2022 American Society of Civil Engineers.
History
Received: Aug 10, 2021
Accepted: Jul 24, 2022
Published online: Oct 25, 2022
Published in print: Jan 1, 2023
Discussion open until: Mar 25, 2023
ASCE Technical Topics:
- Bridge tests
- Comparative studies
- Computer vision and image processing
- Design (by type)
- Engineering fundamentals
- Engineering mechanics
- Field tests
- Foundation design
- Foundations
- Geotechnical engineering
- Load bearing capacity
- Load factors
- Load tests
- Measurement (by type)
- Methodology (by type)
- Research methods (by type)
- Sensors and sensing
- Static loads
- Statics (mechanics)
- Structural design
- Tests (by type)
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