Novel approach to extract dense full-field dynamic parameters of large-scale bridges using spatial sequence video
Abstract
This study proposes the use of a high-speed camera as a holographic visual sensor to obtain the dense full-field dynamic parameters of the main beam of a bridge by the field of view through uniaxial rotation photography. Based on the basic principle that the frequency and mode of a structure are inherent characteristics, the mode coordinates obtained from each field of view are unified, normalized, and matched according to the same name pixels to obtain the dense fullfield dynamic parameters of the entire bridge. The frequency and first three order modes of a self-anchored suspension test bridge are collected by the method proposed in this study. The frequency comparison between the accelerometers and dial gauges is within 3%, and the mode shapes are more holographic and more realistic than those obtained by limited measuring points. In addition, the difference in the curvature mode under various damage conditions obtained by limited measurement points is compared with that obtained by the method proposed in this study. Results shows that the dense full-field modal curvature difference can reflect the change in the damage location even in a low order, which means the sensitivity of the change of damage location in low-order modal.
Keyword : structural health monitoring, holographic visual sensor, uniaxial rotation photography, structural damage identification
How to Cite
Deng, G., Zhou, Z., Shao, S., Chu, X., & Du, P. (2021). Novel approach to extract dense full-field dynamic parameters of large-scale bridges using spatial sequence video. Journal of Civil Engineering and Management, 27(8), 617-636. https://doi.org/10.3846/jcem.2021.15797
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Feng, D., & Feng, M. Q. (2017b). Identification of structural stiffness and excitation forces in time domain using noncontact vision-based dis-placement measurement. Journal of Sound and Vibration, 406, 15–28. https://doi.org/10.1016/j.jsv.2017.06.008
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Jiang T., Frøseth G. T., Rønnquist A., & Fagerholt, E. (2020). A robust line-tracking photogrammetry method for uplift measurements of railway catenary systems in noisy backgrounds. Mechanical Systems and Signal Processing, 144, 106888. https://doi.org/10.1016/j.ymssp.2020.106888
Li, H., Huang, Y., Ou, J., & Bao, Y. (2011). Fractal dimension-based damage detection method for beams with a uniform cross-section. Comput-er-Aided Civil and Infrastructure Engineering, 26(3), 190–206. https://doi.org/10.1111/j.1467-8667.2010.00686.x
Malekjafarian, A., McGetrick, P. J., & OBrien, E. J. (2015). A review of indirect bridge monitoring using passing vehicles. Shock and Vibration, Article ID 286139. https://doi.org/10.1155/2015/286139
Martins, L. L., Rebordão, J. M., & Ribeiro, A. S. (2013). Conception and development of an optical methodology applied to long-distance meas-urement of suspension bridges dynamic displacement. Journal of Physics: Conference Series, 459(1), 012055. https://doi.org/10.1088/1742-6596/459/1/012055
Matarazzo, T. J., & Pakzad, S. N. (2018). Scalable structural modal identification using dynamic sensor network data with STRIDEX. Comput-er-Aided Civil and Infrastructure Engineering, 33(1), 4–20. https://doi.org/10.1111/mice.12298
Matas, J., & Chum, O. (2004). Randomized RANSAC with Td,d test. Image and Vision Computing, 22(10), 837–842. https://doi.org/10.1016/j.imavis.2004.02.009
Mezirow, J. (2014). Perspective transformation. Adult Education, 28(2), 100–110. https://doi.org/10.1177/074171367802800202
Sarrafi, A., Poozesh, P., Niezrecki, C., & Mao, Z. (2017, April). Mode extraction on wind turbine blades via phase-based video motion estimation. In Smart Materials and Nondestructive Evaluation for Energy Systems 2017 (Vol. 10171). International Society for Optics and Photonics. https://doi.org/10.1117/12.2260406
Shao, S., Zhou, Z., Deng, G., Du, P., Jian, C., & Yu, Z. (2020). Experiment of structural geometric morphology monitoring for bridges using hol-ographic visual sensor. Sensors, 20(4), 1187. https://doi.org/10.3390/s20041187
Wadhwa, N., Rubinstein, M., Durand, F., & Freeman, W. T. (2013). Phase-based video motion processing. ACM Transactions on Graphics (TOG), 32(4). https://doi.org/10.1145/2461912.2461966
Wang, S., Zhou, Z., Wen, D., & Huang, Y. (2016). New method for calculating the preoffsetting value of the saddle on suspension bridges consid-ering the influence of more parameters. Journal of Bridge Engineering, 21(12), 06016010. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000956
Xu, Y., & Brownjohn, J. M. (2018). Review of machine-vision based methodologies for displacement measurement in civil structures. Journal of Civil Structural Health Monitoring, 8(1), 91–110. https://doi.org/10.1007/s13349-017-0261-4
Yang, Y. B., Lin, C. W., & Yau, J. D. (2004). Extracting bridge frequencies from the dynamic response of a passing vehicle. Journal of Sound and Vibration, 272(3–5), 471–493. https://doi.org/10.1016/S0022-460X(03)00378-X
Yang, Y., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C., & Mascareñas, D. (2017a). Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification. Mechanical Systems and Signal Processing, 85, 567–590. https://doi.org/10.1016/j.ymssp.2016.08.041
Yang, Y., Dorn, C., Mancini, T., Talken, Z., Nagarajaiah, S., Kenyon, G., Farrar, C., & Mascareñas, D. (2017b). Blind identification of full-field vibration modes of output-only structures from uniformly-sampled, possibly temporally-aliased (sub-Nyquist), video measurements. Journal of Sound and Vibration, 390, 232–256. https://doi.org/10.1016/j.jsv.2016.11.034
Yang, Y., Dorn, C., Mancini, T., Talken, Z., Theiler, J., Kenyon, G., Farrar, C., & Mascarenas, D. (2018a). Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures. Structural Health Monitoring, 17(3), 514–531. https://doi.org/10.1177/1475921717704385
Yang, Y., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C., & Mascareñas, D. (2018b). Spatiotemporal video-domain high-fidelity simula-tion and realistic visualization of full-field dynamic responses of structures by a combination of high-spatial-resolution modal model and video motion manipulations. Structural Control and Health Monitoring, 25(8), e2193. https://doi.org/10.1002/stc.2193
Ye, X. W., Dong, C. Z., & Liu, T. (2016a). Image-based structural dynamic displacement measurement using different multi-object tracking algo-rithms. Smart Structures and Systems, 17(6), 935–956. https://doi.org/10.12989/sss.2016.17.6.935
Ye, X. W., Dong, C. Z., & Liu, T. (2016b). A review of machine vision-based structural health monitoring: methodologies and applications. Jour-nal of Sensors, Article ID 7103039. https://doi.org/10.1155/2016/7103039
Ye, X. W., Ni, Y. Q., Wai, T. T., Wong, K. Y., Zhang, X. M., & Xu, F. (2013). A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification. Smart Structures and Systems, 12(3–4), 363–379. https://doi.org/10.12989/sss.2013.12.3_4.363
Ye, X. W., & Dong, C. Z. (2019). Review of computer vision-based structural displacement monitoring. China Journal of Highway and Transport, 32(11), 21–39.
Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334. https://doi.org/10.1109/34.888718
Zhang, Z. (2004). Camera calibration with one-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(7), 892–899. https://doi.org/10.1109/TPAMI.2004.21
Zhao, J., Bao, Y., Guan, Z., Zuo, W., Li, J., & Li, H. (2019). Video-based multiscale identification approach for tower vibration of a cable-stayed bridge model under earthquake ground motions. Structural Control and Health Monitoring, 26(3), e2314. https://doi.org/10.1002/stc.2314
Chen, J. G., Davis, A., Wadhwa, N., Durand, F., Freeman, W. T., & Buyukozturk, O. (2015, September). Video camera-based vibration measure-ment for condition assessment of civil infrastructure. In NDT-CE International Symposium Non-Destructive Testing in Civil Engineering (pp. 15–17), Berlin, Germany.
Chang, C. C., & Ji, Y. F. (2007). Flexible videogrammetric technique for three-dimensional structural vibration measurement. Journal of Engineer-ing Mechanics, 133(6), 656–664. https://doi.org/10.1061/(ASCE)0733-9399(2007)133:6(656)
Chu, X., Zhou, Z., Deng, G., Duan, X., & Jiang, X. (2019). An overall deformation monitoring method of structure based on tracking deformation contour. Applied Sciences, 9(21), 4532. https://doi.org/10.3390/app9214532
Deng, G., Zhou, Z., Chu, X., Lei, Y., & Xiang X. (2018). Method of bridge deflection deformation based on holographic image contour stacking analysis. Science Technology and Engineering, 18(28), 246–253.
Deng, G., Zhou, Z., Shao, S., Chu, X., & Jian, C. (2020). A novel dense full-field displacement monitoring method based on image sequences and optical flow algorithm. Applied Sciences, 10(6), 2118. https://doi.org/10.3390/app10062118
Dessi, D., & Camerlengo, G. (2015). Damage identification techniques via modal curvature analysis: overview and comparison. Mechanical Sys-tems & Signal Processing, 52–53, 181–205. https://doi.org/10.1016/j.ymssp.2014.05.031
Dong, C. Z., Ye, X. W., & Jin, T. (2018). Identification of structural dynamic characteristics based on machine vision technology. Measurement, 126, 405–416. https://doi.org/10.1016/j.measurement.2017.09.043
Dong, C. Z., Celik, O., & Catbas, F. N. (2019). Marker-free monitoring of the grandstand structures and modal identification using computer vision methods. Structural Health Monitoring, 18(5–6), 1491–1509. https://doi.org/10.1177/1475921718806895
Eshkevari, S. S., Heydari, N., Kutz, J. N., Pakzad, S. N., Diplas, P., & Eshkevari, S. S. (2019). Operational vision-based modal identification of structures: A novel framework. In Proceedings of 12th International Workshop on Structural Health Monitoring. SAFRAN. https://doi.org/10.12783/shm2019/32502
Eshkevari, S. S., Pakzad, S. N., Takáč, M., & Matarazzo, T. J. (2020). Modal identification of bridges using mobile sensors with sparse vibration data. Journal of Engineering Mechanics, 146(4), 04020011. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001733
Feng, D., Feng, M. Q., Ozer, E., & Fukuda, Y. (2015). A vision-based sensor for noncontact structural displacement measurement. Sensors, 15(7), 16557–16575. https://doi.org/10.3390/s150716557
Feng, D., & Feng, M. Q. (2017a). Experimental validation of cost-effective vision-based structural health monitoring. Mechanical Systems and Signal Processing, 88, 199–211. https://doi.org/10.1016/j.ymssp.2016.11.021
Feng, D., & Feng, M. Q. (2017b). Identification of structural stiffness and excitation forces in time domain using noncontact vision-based dis-placement measurement. Journal of Sound and Vibration, 406, 15–28. https://doi.org/10.1016/j.jsv.2017.06.008
Feng, D., Scarangello, T., Feng, M. Q., & Ye, Q. (2017). Cable tension force estimate using novel noncontact vision-based sensor. Measurement, 99, 44–52. https://doi.org/10.1016/j.measurement.2016.12.020
Fleet, D. J., & Jepson, A. D. (1990). Computation of component image velocity from local phase information. International Journal of Computer Vision, 5(1), 77–104. https://doi.org/10.1007/BF00056772
Goldstein, M. (2003). K_n-nearest neighbor classification. IEEE Transactions on Information Theory, 18(5), 627–630. https://doi.org/10.1109/TIT.1972.1054888
Ho, H. N., Lee, J. H., Park, Y. S., & Lee, J. J. (2012). A synchronized multipoint vision-based system for displacement measurement of civil infra-structures. The Scientific World Journal, Article ID 519146. https://doi.org/10.1100/2012/519146
Ji, Y. F., & Chang, C. C. (2008). Nontarget stereo vision technique for spatiotemporal response measurement of line-like structures. Journal of Engineering Mechanics, 134(6), 466–474. https://doi.org/10.1061/(ASCE)0733-9399(2008)134:6(466)
Jiang T., Frøseth G. T., Rønnquist A., & Fagerholt, E. (2020). A robust line-tracking photogrammetry method for uplift measurements of railway catenary systems in noisy backgrounds. Mechanical Systems and Signal Processing, 144, 106888. https://doi.org/10.1016/j.ymssp.2020.106888
Li, H., Huang, Y., Ou, J., & Bao, Y. (2011). Fractal dimension-based damage detection method for beams with a uniform cross-section. Comput-er-Aided Civil and Infrastructure Engineering, 26(3), 190–206. https://doi.org/10.1111/j.1467-8667.2010.00686.x
Malekjafarian, A., McGetrick, P. J., & OBrien, E. J. (2015). A review of indirect bridge monitoring using passing vehicles. Shock and Vibration, Article ID 286139. https://doi.org/10.1155/2015/286139
Martins, L. L., Rebordão, J. M., & Ribeiro, A. S. (2013). Conception and development of an optical methodology applied to long-distance meas-urement of suspension bridges dynamic displacement. Journal of Physics: Conference Series, 459(1), 012055. https://doi.org/10.1088/1742-6596/459/1/012055
Matarazzo, T. J., & Pakzad, S. N. (2018). Scalable structural modal identification using dynamic sensor network data with STRIDEX. Comput-er-Aided Civil and Infrastructure Engineering, 33(1), 4–20. https://doi.org/10.1111/mice.12298
Matas, J., & Chum, O. (2004). Randomized RANSAC with Td,d test. Image and Vision Computing, 22(10), 837–842. https://doi.org/10.1016/j.imavis.2004.02.009
Mezirow, J. (2014). Perspective transformation. Adult Education, 28(2), 100–110. https://doi.org/10.1177/074171367802800202
Sarrafi, A., Poozesh, P., Niezrecki, C., & Mao, Z. (2017, April). Mode extraction on wind turbine blades via phase-based video motion estimation. In Smart Materials and Nondestructive Evaluation for Energy Systems 2017 (Vol. 10171). International Society for Optics and Photonics. https://doi.org/10.1117/12.2260406
Shao, S., Zhou, Z., Deng, G., Du, P., Jian, C., & Yu, Z. (2020). Experiment of structural geometric morphology monitoring for bridges using hol-ographic visual sensor. Sensors, 20(4), 1187. https://doi.org/10.3390/s20041187
Wadhwa, N., Rubinstein, M., Durand, F., & Freeman, W. T. (2013). Phase-based video motion processing. ACM Transactions on Graphics (TOG), 32(4). https://doi.org/10.1145/2461912.2461966
Wang, S., Zhou, Z., Wen, D., & Huang, Y. (2016). New method for calculating the preoffsetting value of the saddle on suspension bridges consid-ering the influence of more parameters. Journal of Bridge Engineering, 21(12), 06016010. https://doi.org/10.1061/(ASCE)BE.1943-5592.0000956
Xu, Y., & Brownjohn, J. M. (2018). Review of machine-vision based methodologies for displacement measurement in civil structures. Journal of Civil Structural Health Monitoring, 8(1), 91–110. https://doi.org/10.1007/s13349-017-0261-4
Yang, Y. B., Lin, C. W., & Yau, J. D. (2004). Extracting bridge frequencies from the dynamic response of a passing vehicle. Journal of Sound and Vibration, 272(3–5), 471–493. https://doi.org/10.1016/S0022-460X(03)00378-X
Yang, Y., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C., & Mascareñas, D. (2017a). Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification. Mechanical Systems and Signal Processing, 85, 567–590. https://doi.org/10.1016/j.ymssp.2016.08.041
Yang, Y., Dorn, C., Mancini, T., Talken, Z., Nagarajaiah, S., Kenyon, G., Farrar, C., & Mascareñas, D. (2017b). Blind identification of full-field vibration modes of output-only structures from uniformly-sampled, possibly temporally-aliased (sub-Nyquist), video measurements. Journal of Sound and Vibration, 390, 232–256. https://doi.org/10.1016/j.jsv.2016.11.034
Yang, Y., Dorn, C., Mancini, T., Talken, Z., Theiler, J., Kenyon, G., Farrar, C., & Mascarenas, D. (2018a). Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures. Structural Health Monitoring, 17(3), 514–531. https://doi.org/10.1177/1475921717704385
Yang, Y., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C., & Mascareñas, D. (2018b). Spatiotemporal video-domain high-fidelity simula-tion and realistic visualization of full-field dynamic responses of structures by a combination of high-spatial-resolution modal model and video motion manipulations. Structural Control and Health Monitoring, 25(8), e2193. https://doi.org/10.1002/stc.2193
Ye, X. W., Dong, C. Z., & Liu, T. (2016a). Image-based structural dynamic displacement measurement using different multi-object tracking algo-rithms. Smart Structures and Systems, 17(6), 935–956. https://doi.org/10.12989/sss.2016.17.6.935
Ye, X. W., Dong, C. Z., & Liu, T. (2016b). A review of machine vision-based structural health monitoring: methodologies and applications. Jour-nal of Sensors, Article ID 7103039. https://doi.org/10.1155/2016/7103039
Ye, X. W., Ni, Y. Q., Wai, T. T., Wong, K. Y., Zhang, X. M., & Xu, F. (2013). A vision-based system for dynamic displacement measurement of long-span bridges: algorithm and verification. Smart Structures and Systems, 12(3–4), 363–379. https://doi.org/10.12989/sss.2013.12.3_4.363
Ye, X. W., & Dong, C. Z. (2019). Review of computer vision-based structural displacement monitoring. China Journal of Highway and Transport, 32(11), 21–39.
Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334. https://doi.org/10.1109/34.888718
Zhang, Z. (2004). Camera calibration with one-dimensional objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(7), 892–899. https://doi.org/10.1109/TPAMI.2004.21
Zhao, J., Bao, Y., Guan, Z., Zuo, W., Li, J., & Li, H. (2019). Video-based multiscale identification approach for tower vibration of a cable-stayed bridge model under earthquake ground motions. Structural Control and Health Monitoring, 26(3), e2314. https://doi.org/10.1002/stc.2314