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Review for vision-based structural damage evaluation in disasters focusing on nonlinearity

  • Sifan Wang (Department of Engineering Mechanics and Energy, University of Tsukuba) ;
  • Mayuko Nishio (Institute of Systems and Information Engineering, University of Tsukuba)
  • Received : 2023.10.27
  • Accepted : 2024.03.28
  • Published : 2024.04.25
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Abstract

With the increasing diversity of internet media, available video data have become more convenient and abundant. Related video data-based research has advanced rapidly in recent years owing to advantages such as noncontact, low-cost data acquisition, high spatial resolution, and simultaneity. Additionally, structural nonlinearity extraction has attracted increasing attention as a tool for damage evaluation. This review paper aims to summarize the research experience with the recent developments and applications of video data-based technology for structural nonlinearity extraction and damage evaluation. The most regularly used object detection images and video databases are first summarized, followed by suggestions for obtaining video data on structural nonlinear damage events. Technologies for linear and nonlinear system identification based on video data are then discussed. In addition, common nonlinear damage types in disaster events and prevalent processing algorithms are reviewed in the section on structural damage evaluation using video data uploaded on online platform. Finally, a discussion regarding some potential research directions is proposed to address the weaknesses of the current nonlinear extraction technology based on video data, such as the use of uni-dimensional time-series data as leverage to further achieve nonlinear extraction and the difficulty of real-time detection, including the fields of nonlinear extraction for spatial data, real-time detection, and visualization.

Keywords

Acknowledgement

The study described in this paper was supported by the JST SPRING Program (grant number JPMJSP2124) and the JST FOREST Program (grant number JPMJFR205T). The authors also thank to use of the video data on "Archives of E-Defense Shakingtable Experimentation Database and Information (ASEBI)", National Research Institute for Earth Science and Disaster Resilience (NIED), Japan.

References

  1. Abdulkareem, M., Bakhary, N., Vafaei, M., Noor, N.M. and Padil, K.H. (2018), "Non-probabilistic wavelet method to consider uncertainties in structural damage detection", J. Sound Vib., 433, 77-98. https://doi.org/10.1016/j.jsv.2018.07.011 
  2. Abu-El-Haija, S., Kothari, N., Lee, J., Natsev, P., Toderici, G., Varadarajan, B. and Vijayanarasimhan, S. (2016), "Youtube-8m: A large-scale video classification benchmark", arXiv preprint arXiv, 1609.08675. https://doi.org/10.48550/arXiv.1609.08675 
  3. Agathos, K., Chatzi, E. and Bordas, SPA. (2018), "Multiple crack detection in 3D using a stable XFEM and global optimization", Compos. Mech., 62(4), 835-852. https://doi.org/10.1007/s00466-017-1532-y 
  4. Alamdari, M.M., Ge, L., Kildashti, K., Zhou, Y., Harvey, B. and Du, Z. (2019), "Non-contact structural health monitoring of a cable-stayed bridge: Case study", Struct. Infrastruct. Eng., 15(8), 1119-1136. https://doi.org/10.1080/15732479.2019.1609529 
  5. Al-Taie, M.Z., Kadry, S. and Lucas, J.P. (2019), "Online data preprocessing: A case study approach", Int. J. Electr. Comput. Eng., 9(4), 2620. https://doi.org/10.11591/ijece.v9i4.pp2620-2626 
  6. Altunisik, A.C., Okur, F.Y., Karaca, S. and Kahya, V. (2019), "Vibration-based damage detection in beam structures with multiple cracks: Modal curvature vs. modal flexibility methods", Nondestr. Test Eval., 34(1), 33-53. https://doi.org/10.1080/10589759.2018.1518445 
  7. Antoni, J. and Chauhan, S. (2013), "A study and extension of second-order blind source separation to operational modal analysis", J. Sound Vib., 332(4), 1079-1106. https://doi.org/10.1016/j.jsv.2012.09.016 
  8. Aoi, S., Asano, Y., Kunugi, T., Kimura, T., Uehira, K., Takahashi, N., Ueda, H., Shiomi, K., Matsumoto, T. and Fujiwara, H. (2020), "MOWLAS: NIED observation network for earthquake, tsunami and volcano", Earth Planets Space, 72(1), 1-31. https://doi.org/10.1186/s40623-020-01250-x 
  9. Arbabi, H. and Mezic, I. (2017), "Ergodic theory, dynamic mode decomposition, and computation of spectral properties of the Koopman operator", SIAM J. Appl. Dyn. Syst., 16(4), 2096-2126. https://doi.org/10.1137/17M1125236 
  10. Asgarieh, E., Moaveni, B. and Stavridis, A. (2014), "Nonlinear finite element model updating of an infilled frame based on identified time-varying modal parameters during an earthquake", J. Sound Vib., 333(23), 6057-6073. https://doi.org/10.1016/j.jsv.2014.04.064 
  11. Astroza, R., Ebrahimian, H. and Conte, J.P. (2019), "Performance comparison of Kalman-based filters for nonlinear structural finite element model updating", J. Sound Vib., 438, 520-542. https://doi.org/10.1016/j.jsv.2018.09.023 
  12. Avci, O., Abdeljaber, O., Kiranyaz, S., Hussein, M., Gabbouj, M. and Inman, D.J. (2021), "A review of vibration-based damage detection in civil structures: From traditional methods to Machine Learning and Deep Learning applications", Mech. Syst. Signal Process., 147, 107077. https://doi.org/10.1016/j.ymssp.2020.107077 
  13. Bhowmick, S. and Nagarajaiah, S. (2020), "Identification of full-field dynamic modes using continuous displacement response estimated from vibrating edge video", J. Sound Vib., 489, 115657. https://doi.org/10.1016/j.jsv.2020.115657 
  14. Bhowmick, S., Nagarajaiah, S. and Lai, Z. (2020), "Measurement of full-field displacement time history of a vibrating continuous edge from video", Mech. Syst. Signal Process., 144, 106847. https://doi.org/10.1016/j.ymssp.2020.106847 
  15. Brandes, U. (2001), "A faster algorithm for betweenness centrality", J. Math Sociol., 25(2), 163-177. https://doi.org/10.1080/0022250X.2001.9990249 
  16. Brewick, P.T. and Smyth, A.W. (2014), "On the application of blind source separation for damping estimation of bridges under traffic loading", J. Sound Vib., 333(26), 7333-7351. https://doi.org/10.1016/j.jsv.2014.08.010 
  17. Cha, Y.J., Choi, W., Suh, G., Mahmoudkhani, S. and Buyukozturk, O. (2018), "Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types", Comput.-Aided Civil Infrastruct. Eng., 33(9), 731-747. https://doi.org/10.1111/mice.12334 
  18. Chaudhry, R., Ravichandran, A., Hager, G. and Vidal, R. (2009), "Histograms of oriented optical flow and binet-cauchy kernels on nonlinear dynamical systems for the recognition of human actions", In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE Publications, pp. 1932-1939. https://doi.org/10.1109/CVPR.2009.5206821 
  19. Chen, Y. and Yang, H. (2016), "Heterogeneous recurrence representation and quantification of dynamic transitions in continuous nonlinear processes", Eur. Phys. J. B., 89, 1-11. https://doi.org/10.1140/epjb/e2016-60850-y 
  20. Chen, J.G., Wadhwa, N., Cha, Y.J., Durand, F., Freeman, W.T. and Buyukozturk, O. (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 
  21. Chen, C.B., Yang, H. and Kumara, S. (2018), "Recurrence network modeling and analysis of spatial data", Chaos, 28(8), 085714. https://doi.org/10.1063/1.5024917 
  22. Chen, Y., Qian, Z., Chen, K., Tan, P. and Tesfamariam, S. (2019), "Seismic performance of a nonlinear energy sink with negative stiffness and sliding friction", Struct. Control Health Monit., 26(11), e2437. https://doi.org/10.1002/stc.2437 
  23. Chen, Z., Wang, Y., Wu, J., Deng, C. and Hu, K. (2021), "Sensor data-driven structural damage detection based on deep convolutional neural networks and continuous wavelet transform", Appl. Intell., 51(8), 5598-5609. https://doi.org/10.1007/s10489-020-02092-6 
  24. Cheng, L. (2021), "Digital video image preprocessing algorithm based on embedded system", J. Phys. Conf. Series, 2074(1), 012004. https://iopscience.iop.org/article/10.1088/17426596/2074/1/012004/meta 
  25. Cheng, C.M., Peng, Z.K., Dong, X.J., Zhang, W.M. and Meng, G. (2014), "Locating non-linear components in two dimensional periodic structures based on NOFRFs", Int. J. Non-Linear Mech., 67, 198-208. https://doi.org/10.1016/j.ijnonlinmec.2014.09.004 
  26. Chiu, L.N.S., Falzon, B.G., Ruan, D., Xu, S., Thomson, R.S., Chen, B. and Yan, W. (2015), "Crush responses of composite cylinder under quasi-static and dynamic loading", Compos. Struct., 131, 90-98. https://doi.org/10.1016/j.compstruct.2015.04.057 
  27. Choi, I., Kim, J. and Kim, D. (2016), "A target-less vision-based displacement sensor based on image convex hull optimization for measuring the dynamic response of building structures", Sensors (Basel), 16(12), 2085. https://doi.org/10.3390/s16122085 
  28. Chung, Y.L., Nagae, T., Hitaka, T. and Nakashima, M. (2010), "Seismic resistance capacity of high-rise buildings subjected to long-period ground motions: E-Defense shaking table test", J. Struct. Eng., 136(6), 637-644. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000161 
  29. Colin, M., Thomas, O., Grondel, S. and Cattan, E. (2020), "Very large amplitude vibrations of flexible structures: Experimental identification and validation of a quadratic drag damping model", J. Fluids Struct., 97, 103056. https://doi.org/10.1016/j.jfluidstructs.2020.103056 
  30. Dasari, S., Dorn, C., Yang, Y., Larson, A. and Mascarenas, D. (2018), "A framework for the identification of full-field structural dynamics using sequences of images in the presence of non-ideal operating conditions", J. Intell. Mater. Syst. Struct., 29(17), 3456- 3481. https://doi.org/10.1177/1045389X17754271 
  31. Dong, C.Z. and Catbas, F.N. (2021), "A review of computer vision-based structural health monitoring at local and global levels", Struct. Health Monit., 20(2), 692-743. https://doi.org/10.1177/1475921720935585 
  32. Dunphy, K., Fekri, M.N., Grolinger, K. and Sadhu, A. (2022), "Data augmentation for deep-learning-based multiclass structural damage detection using limited information", Sensors (Basel), 22(16), 6193. https://doi.org/10.3390/s22166193 
  33. Eckmann, J.P., Kamphorst, S.O. and Ruelle, D. (1987), "Recurrence plots of dynamical systems", Europhys. Lett., 4(9), 973-977. https://doi.org/10.1209/0295-5075/4/9/004 
  34. EM-DAT (2023), The International Disaster Database. Institute of Health and Society (IRSS), Brussels, Belgium. https://www.emdat.be [Accessed in October 2023] 
  35. Fallahian, M., Ahmadi, E. and Khoshnoudian, F. (2022), "A structural damage detection algorithm based on discrete wavelet transform and ensemble pattern recognition models", J. Civil Struct. Health Monit., 12(2), 323-338. https://doi.org/10.1007/s13349-021-00546-0 
  36. Fan, W. and Qiao, P. (2011), "Vibration-based damage identification methods: A review and comparative study", Struct. Health Monit., 10(1), 83-111. https://doi.org/10.1177/1475921710365419 
  37. Farneback, G. (2003), "Two-frame motion estimation based on polynomial expansion", Proceedings of Image Analysis: 13th Scandinavian Conference, SCIA 2003, Halmstad, Sweden, June-July, Vol. 13, pp. 363-370. https://doi.org/10.1007/3-540-45103-X_50 
  38. Farrar, C.R., Worden, K., Todd, M.D., Park, G., Nichols, J., Adams, D.E., Bement, M.T. and Farinholt, K. (2007), "Nonlinear system identification for damage detection (No. LA-14353-MS)", Los Alamos National Lab. (LANL), Los Alamos, NM, USA. 
  39. Farrar, C., Nishio, M., Hemez, F., Stull, C., Park, G., Cornwell, P. and Worden, K. (2016), "Feature extraction for structural dynamics model validation (No. LA-UR-16-20151)", Los Alamos National Lab. (LANL), Los Alamos, NM, USA. 
  40. Feldman, M. (1994a), "Non-linear system vibration analysis using Hilbert transform--I. Free vibration analysis method "Freevib"", Mech. Syst. Signal Process., 8(2), 119-127. https://doi.org/10.1006/mssp.1994.1011 
  41. Feldman, M. (1994b), "Non-linear system vibration analysis using Hilbert transform--II. Forced vibration analysis method "Forcevib"", Mech. Syst. Signal Process., 8(3), 309-318. https://doi.org/10.1006/mssp.1994.1023 
  42. Feldman, M. (2014), "Hilbert transform methods for nonparametric identification of nonlinear time varying vibration systems", Mech. Syst. Signal Process., 47(1-2), 66-77. https://doi.org/10.1016/j.ymssp.2012.09.003 
  43. Feng, D. and Feng, M.Q. (2016), "Vision-based multipoint displacement measurement for structural health monitoring", Struct. Control Health Monit., 23(5), 876-890. https://doi.org/10.1002/stc.1819 
  44. Feng, D., Feng, M.Q., Ozer, E. and Fukuda, Y. (2015), "A vision-based sensor for noncontact structural displacement measurement", Sensors (Basel), 15(7), 16557-16575. https://doi.org/10.3390/s150716557 
  45. Fleet, D.J. and Jepson, A.D. (1990), "Computation of component image velocity from local phase information", Int. J. Comput. Vision., 5(1), 77-104. https://doi.org/10.1007/BF00056772 
  46. Frigui, F., Faye, J.P., Martin, C., Dalverny, O., Peres, F. and Judenherc, S. (2018), "Global methodology for damage detection and localization in civil engineering structures", Eng. Struct., 171, 686-695. https://doi.org/10.1016/j.engstruct.2018.06.026 
  47. Goyal, R., Ebrahimi, Kahou, S., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M. and Memisevic, R. (2017), "The "something something" video database for learning and evaluating visual common sense", Proceedings of the IEEE International Conference on Computer Vision, pp. 5842-5850. https://openaccess.thecvf.com/content_iccv_2017/html/Goyal_The_Something_Something_ICCV_2017_paper.html 
  48. Grotto, F., Rivallant, S. and Bouvet, C. (2022), "Development of a 3D finite element model at mesoscale for the crushing of unidirectional composites: Application to plates crushing", Compos. Struct., 287, 115346. https://doi.org/10.1016/j.compstruct.2022.115346 
  49. Horiuchi, T., Ohsaki, M., Kurata, M., Ramirez, J.A., Yamashita, T., and Kajiwara, K. (2022), "Contributions of E-Defense Shaking Table to Earthquake Engineering and its Future", J. Disaster Res., 17(6), 985-999. https://doi.org/10.20965/jdr.2022.p0985 
  50. Hou, R., Xia, Y. and Zhou, X. (2018), "Structural damage detection based on l1 regularization using natural frequencies and mode shapes", Struct. Control Health Monit., 25(3), e2107. https://doi.org/10.1002/stc.2107 
  51. Huynh, T.C., Park, J.H., Jung, H.J. and Kim, J.T. (2019), "Quasi-autonomous bolt-loosening detection method using vision-based deep learning and image processing", Autom. Constr., 105, 102844. https://doi.org/10.1016/j.autcon.2019.102844 
  52. Jiao, J., Guo, J., Fujita, K. and Takewaki, I. (2021), "Displacement measurement and nonlinear structural system identification: A vision-based approach with camera motion correction using planar structures", Struct. Control Health Monit., 28(8), e2761. https://doi.org/10.1002/stc.2761 
  53. Kaewunruen, S., Ngamkhanong, C. and Xu, S. (2020), "Large amplitude vibrations of imperfect spider web structures", Sci. Rep., 10(1), 19161. https://doi.org/10.1038/s41598-020-76269-x 
  54. Kang, D. and Cha, Y.J. (2018), "Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging", Comput.-Aided Civil Infrastruct. Eng., 33(10), 885-902. https://doi.org/10.1111/mice.12375 
  55. Khaloo, A. and Lattanzi, D. (2017), "Pixel-wise structural motion tracking from rectified repurposed videos", Struct. Control Health Monit., 24(11), e2009. https://doi.org/10.1002/stc.2009 
  56. Khatir, S., Abdel, Wahab, M.A., Boutchicha, D. and Khatir, T. (2019), "Structural health monitoring using modal strain energy damage indicator coupled with teaching-learning-based optimization algorithm and isogoemetric analysis", J. Sound Vib., 448, 230-246. https://doi.org/10.1016/j.jsv.2019.02.017 
  57. Khuc, T. and Catbas, F.N. (2017), "Completely contactless structural health monitoring of real-life structures using cameras and computer vision", Struct. Control Health Monit., 24(1), e1852. https://doi.org/10.1002/stc.1852 
  58. Kordestani, H. and Zhang, C. (2020), "Direct use of the savitzky-golay filter to develop an output-only trend line-based damage detection method", Sensors (Basel), 20(7). https://doi.org/10.3390/s20071983
  59. Kumar, P., Batchu, S. and Kota, S.R. (2021), Real-time concrete damage detection using deep learning for high rise structures. IEEE Access, 9, 112312-112331. https://doi.org/10.1109/ACCESS.2021.3102647 
  60. Kuok, S.C., Yuen, K.V., Girolami, M. and Roberts, S. (2022), "Broad learning robust semi-active structural control: A nonparametric approach", Mech. Syst. Signal Process., 162, 108012. https://doi.org/10.1016/j.ymssp.2021.108012 
  61. Kwok, H.K. and Jones, D.L. (2000), "Improved instantaneous frequency estimation using an adaptive short-time Fourier transform", IEEE Trans. Signal Process., 48(10), 2964-2972. https://doi.org/10.1109/78.869059 
  62. Lacy, S.L. and Bernstein, D.S. (2005), "Subspace identification for non-linear systems with measured-input non-linearities", Int. J. Control., 78(12), 906-926. https://doi.org/10.1080/00207170500214095 
  63. Lee, H.M. and Park, H.S. (2011), "Gage-free stress estimation of a beam-like structure based on terrestrial laser scanning", Comput.-Aided Civil Infrastruct. Eng., 26(8), 647-658. https://doi.org/10.1111/j.1467-8667.2011.00723.x 
  64. Lee, S.Y., Nguyen, K.D., Huynh, T.C., Kim, J.T., Yi, J.H. and Han, S.H. (2012), "Vibration-based damage monitoring of harbor caisson structure with damaged foundation-structure interface", Smart Struct. Syst., Int. J., 10(6), 517-546. https://doi.org/10.12989/sss.2012.10.6.517 
  65. Lee, J., Natsev, A., Reade, W., Sukthankar, R. and Toderici, G. (2019), "The 2nd youtube-8m large-scale video understanding challenge", Proceedings of the European conference on Computer Vision (ECCV Workshops), pp. 193-205. https://doi.org/10.1007/978-3-030-11018-5_18 
  66. Lee, S.Y., Kim, H., Higuchi, H. and Ishikawa, M. (2021), "Visualization method for the cell-level vesicle transport using optical flow and a diverging colormap", Sensors, 21(2), 522. https://doi.org/10.3390/s21020522 
  67. Li, S., Wu, C. and Kong, F. (2019), "Shaking table model test and seismic performance analysis of a high-rise RC shear wall structure", Shock Vib., 2019, 1-17. https://doi.org/10.1155/2019/6189873 
  68. Liu, L., Mi, J., Zhang, Y. and Lei, Y. (2021), "Damage detection of bridge structures under unknown seismic excitations using support vector machine based on transmissibility function and wavelet packet energy", Smart Struct. Syst., Int. J., 27(2), 257-266. https://doi.org/10.12989/sss.2021.27.2.257 
  69. Luengo, J., Garcia-Gil, D., Ramirez-Gallego, S., Garcia, S., and Herrera, F. (2020), "Big data preprocessing", Cham: Springer. https://doi.org/10.1007/978-3-030-39105-8 
  70. Magalhaes, F., Cunha, A. and Caetano, E. (2012), "Vibration based structural health monitoring of an arch bridge: From automated OMA to damage detection", Mech. Syst. Signal Process., 28, 212-228. https://doi.org/10.1016/j.ymssp.2011.06.011 
  71. Mahdisoltani, F., Berger, G., Gharbieh, W., Fleet, D. and Memisevic, R. (2018), "On the effectiveness of task granularity for transfer learning", arXiv preprint arXiv.,1804.09235. https://doi.org/10.48550/arXiv.1804.09235 
  72. Marchesiello, S. and Garibaldi, L. (2008), "A time domain approach for identifying nonlinear vibrating structures by subspace methods", Mech. Syst. Signal Process., 22(1), 81-101. https://doi.org/10.1016/j.ymssp.2007.04.002 
  73. Masri, S.F. and Caughey, T.K. (1979), "A nonparametric identification technique for nonlinear dynamic problems", J. Appl. Mech., 46(2), 433-447. https://doi.org/10.1115/1.3424568 
  74. Masri, S.F., Sassi, H. and Caughey, T.K. (1982), "Nonparametric identification of nearly arbitrary nonlinear systems", J. Appl. Mech., 49(3), 619-628. https://doi.org/10.1115/1.3162537 
  75. Mousavi, A.A., Zhang, C., Masri, S.F. and Gholipour, G. (2021), "Damage detection and localization of a steel truss bridge model subjected to impact and white noise excitations using empirical wavelet transform neural network approach", Measurement, 185, 110060. https://doi.org/10.1016/j.measurement.2021.110060 
  76. National Research Institute for Earth Science and Disaster Resilience (NIED) (2023), "ASEBI: Archives of E-Defense Shaking table Experimentation Database and Information."  https://doi.org/10.17598/nied.0020 [Accessed in October 2023] 
  77. Nelles, O. (2020), "Nonlinear system identification: From classical approaches to neural networks, fuzzy models, and gaussian processes", Springer nature. https://doi.org/10.1007/978-3-030-47439-3 
  78. Newman, M.E.J. (2005), "A measure of betweenness centrality based on random walks", Soc. Netw., 27(1), 39-54. https://doi.org/10.1016/j.socnet.2004.11.009 
  79. Ngo, N. and Robertson, I.N. (2012), "Video analysis of the March 2011 tsunami in Japan's coastal cities", Research rep. UHM/CEE, 12. 
  80. Norio, O., Ye, T., Kajitani, Y., Shi, P. and Tatano, H. (2011), "The 2011 eastern Japan great earthquake disaster: Overview and comments", Int. J. Disaster Risk. Sci., 2(1), 34-42. https://doi.org/10.1007/s13753-011-0004-9 
  81. Nyquist, H. (1928), "Certain topics in telegraph transmission theory", Trans. Am. Inst. Electr. Eng., 47(2), 617-644. https://doi.org/10.1109/T-AIEE.1928.5055024 
  82. Oliveira, C.S. and Ferreira, M.A. (2021), "Following the video surveillance and personal video cameras: New tools and innovations to health monitor the earthquake wave field", Int. J. Disaster Risk Reduc., 64, 102489. https://doi.org/10.1016/j.ijdrr.2021.102489 
  83. Oppenheim, A.V. (1965), "Superposition in a class of nonlinear systems", Technical report 432. 
  84. Oster, G. and Nishijima, Y. (1963), "Moire patterns", Sci. Am., 208(5), 54-63. https://doi.org/10.1038/scientificamerican0563-54 
  85. Oster, G., Wasserman, M. and Zwerling, C. (1964), "Theoretical interpretation of moire patterns", Josa., 54(2), 169-175. https://doi.org/10.1364/JOSA.54.000169 
  86. 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. https://doi.org/10.1016/j.ndteint.2015.12.006 
  87. Peng, Z.K., Lang, Z.Q. and Billings, S.A. (2007), "Crack detection using nonlinear output frequency response functions", J. Sound Vib., 301(3-5), 777-788. https://doi.org/10.1016/j.jsv.2006.10.039 
  88. Pnevmatikos, N.G. and Hatzigeorgiou, G.D. (2017), "Damage detection of framed structures subjected to earthquake excitation using discrete wavelet analysis", Bull. Earthq. Eng., 15(1), 227-248. https://doi.org/10.1007/s10518-016-9962-z 
  89. Proctor, J.L., Brunton, S.L. and Kutz, J.N. (2016), "Dynamic mode decomposition with control", SIAM J. Appl. Dyn. Syst., 15(1), 142-161. https://doi.org/10.1137/15M1013857 
  90. Redmon, J. and Farhadi, A. (2018), "Yolov3: An incremental improvement", arXiv preprint arXiv, 1804.02767. https://doi.org/10.48550/arXiv.1804.02767 
  91. Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016), "You only look once: Unified, real-time object detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779-788. https://doi.org/10.1109/CVPR.2016.91 
  92. Ren, W.X. and Roeck, G.D. (2002a), "Structural damage identification using modal data. I: Simulation verification", J. Struct. Eng., 128(1), 87-95. htps://doi.org/10.1061/(ASCE)0733-9445(2002)128:1(87) 
  93. Ren, W.X. and Roeck, G.D. (2002b), "Structural damage identification using modal Data. II: Test Verification", J. Struct. Eng., 128(1), 96-104. https://doi.org/10.1061/(ASCE)0733-9445(2002)128:1(96)
  94. Ribeiro, D., Calcada, R., Ferreira, J. and Martins, T. (2014), "Non-contact measurement of the dynamic displacement of railway bridges using an advanced video-based system", Eng. Struct., 75, 164-180. https://doi.org/10.1016/j.engstruct.2014.04.051 
  95. Ribeiro, D., Santos, R., Cabral, R., Saramago, G., Montenegro, P., Carvalho, H., Correia, J. and Calcada, R. (2021), "Non-contact structural displacement measurement using Unmanned Aerial Vehicles and video-based systems", Mech. Syst. Signal Process., 160, 107869. https://doi.org/10.1016/j.ymssp.2021.107869 
  96. Rice, H.J. and Fitzpatrick, J.A. (1988), "A generalised technique for spectral analysis of non-linear systems", Mech. Syst. Signal Process., 2(2), 195-207. https://doi.org/10.1016/0888-3270(88)90043-X 
  97. Rice, H.J. and Fitzpatrick, J.A. (1991), "The measurement of nonlinear damping in single-degree-of-freedom systems", J. Vib. Acoust., 113(1), 132-140. https://doi.org/10.1115/1.2930147 
  98. Rowley, C.W., Mezic, I., Bagheri, S., Schlatter, P. and Henningson, D.S. (2009), "Spectral analysis of nonlinear flows", J. Fluid. Mech., 641, 115-127. https://doi.org/10.1017/S0022112009992059 
  99. Rubio, L. and Fernandez-Saez, J. (2012), "A new efficient procedure to solve the nonlinear dynamics of a cracked rotor", Nonlinear Dyn., 70, 1731-1745. https://doi.org/10.1007/s11071-012-0569-x 
  100. Salehi, H. and Burgueno, R. (2018), "Emerging artificial intelligence methods in structural engineering", Eng. Struct., 171, 170-189. https://doi.org/10.1016/j.engstruct.2018.05.084 
  101. Sarrafi, A., Mao, Z., Niezrecki, C. and Poozesh, P. (2018), "Vibration-based damage detection in wind turbine blades using Phase-based Motion Estimation and motion magnification", J. Sound Vib., 421, 300-318. https://doi.org/10.1016/j.jsv.2018.01.050 
  102. Sathyamoorthy, M. (2017), "Nonlinear analysis of structures", CRC Press. https://doi.org/10.1201/9780203711255 
  103. Serra, R. and Lopez, L. (2017), "Damage detection methodology on beam-like structures based on combined modal Wavelet Transform strategy", Mech. Ind., 18(8), 807. https://doi.org/10.1051/meca/2018007 
  104. Sha, G., Radzienski, M., Cao, M. and Ostachowicz, W. (2019), "A novel method for single and multiple damage detection in beams using relative natural frequency changes", Mech. Syst. Signal Process., 132, 335-352. https://doi.org/10.1016/j.ymssp.2019.06.027 
  105. Shakeel, A., Kirichek, A. and Chassagne, C. (2020), "Yield stress measurements of mud sediments using different rheological methods and geometries: An evidence of two-step yielding", Mar. Geol., 427, 106247. https://doi.org/10.1016/j.margeo.2020.106247 
  106. Shokrani, Y., Dertimanis, V.K., Chatzi, E.N. and Savoia, N. (2018), "On the use of mode shape curvatures for damage localization under varying environmental conditions", Struct. Control Health Monit., 25(4), e2132. https://doi.org/10.1002/stc.2132 
  107. Simoen, E., Roeck, G. and Lombaert, G. (2015), "Dealing with uncertainty in model updating for damage assessment: A review", Mech. Syst. Signal Process., 56-57, 123-149. https://doi.org/10.1016/j.ymssp.2014.11.001 
  108. Simon, M. and Tomlinson, G.R. (1984), "Use of the Hilbert transform in modal analysis of linear and non-linear structures", J. Sound Vib., 96(4), 421-436. https://doi.org/10.1016/0022-460X(84)90630-8 
  109. Sirca, J.G. and Adeli, H. (2012), "System identification in structural engineering", Sci. Iran., 19(6), 1355-1364. https://doi.org/10.1016/j.scient.2012.09.002 
  110. Smith, A.R. (1978), "Color gamut transform pairs", ACM Siggraph Computer Graphics, 12(3), 12-19. https://doi.org/10.1145/965139.807361 
  111. Smyl, D., Pour-Ghaz, M. and Seppanen, A. (2018), "Detection and reconstruction of complex structural cracking patterns with electrical imaging", NDT E Int., 99, 123-133. https://doi.org/10.1016/j.ndteint.2018.06.004 
  112. Staszewski, W.J., bin, J.R., Klepka, A., Szwedo, M. and Uhl, T. (2012), "A review of laser Doppler vibrometry for structural health monitoring applications", Key Eng. Mater., 518, 1-15. https://doi.org/10.4028/www.scientific.net/KEM.518.1 
  113. Swaminathan, K., Naveenkumar, D.T., Zenkour, A.M. and Carrera, E. (2015), "Stress, vibration and buckling analyses of FGM plates-A state-of-the-art review", Compos. Struct., 120, 10-31. https://doi.org/10.1016/j.compstruct.2014.09.070 
  114. Tashakori, S., Baghalian, A., Unal, M., Fekrmandi, H., senyurek, D., McDaniel, D. and Tansel, I.N. (2016), "Contact and non-contact approaches in load monitoring applications using surface response to excitation method", Measurement, 89, 197-203. https://doi.org/10.1016/j.measurement.2016.04.013 
  115. Tian, H. and Chen, S.C. (2017), "MCA-NN: Multiple correspondence analysis based neural network for disaster information detection", IEEE Third International Conference on Multimedia Big Data (BigMM). IEEE Publications, pp. 268-275. https://doi.org/10.1109/BigMM.2017.30 
  116. Touze, C., Vizzaccaro, A. and Thomas, O. (2021), "Model order reduction methods for geometrically nonlinear structures: A review of nonlinear techniques", Nonlinear Dyn., 105(2), 1141-1190. https://doi.org/10.1007/s11071-021-06693-9 
  117. Verboven, P., Guillaume, P., Vanlanduit, S. and Cauberghe, B. (2006), "Assessment of nonlinear distortions in modal testing and analysis of vibrating automotive structures", J. Sound Vib., 293(1-2), 299-319. https://doi.org/10.1016/j.jsv.2005.09.039 
  118. Wadhwa, N., Rubinstein, M., Durand, F. and Freeman, W.T. (2013), "Phase-based video motion processing", ACM Trans. Graph., 32(4), 1-10. https://doi.org/10.1145/2461912.2461966
  119. Wang, C., Ai, D. and Ren, W.X. (2019), "A wavelet transform and substructure algorithm for tracking the abrupt stiffness degradation of shear structure", Adv. Struct. Eng., 22(5), 1136-1148. https://doi.org/10.1177/1369433218807690 
  120. Wang, N., Zhao, X., Zou, Z., Zhao, P. and Qi, F. (2020), "Autonomous damage segmentation and measurement of glazed tiles in historic buildings via deep learning", Comput.-Aided Civil Infrastruct. Eng., 35(3), 277-291. https://doi.org/10.1111/mice.12488 
  121. Wickramasinghe, W.R., Thambiratnam, D.P. and Chan, T.H.T. (2020), "Damage detection in a suspension bridge using modal flexibility method", Eng. Fail Anal., 107, 104194. https://doi.org/10.1016/j.engfailanal.2019.104194 
  122. Wu, Y. and Chen, X. (2020), "Identification of nonlinear aerodynamic damping from stochastic crosswind response of tall buildings using unscented Kalman filter technique", Eng. Struct., 220, 110791. https://doi.org/10.1016/j.engstruct.2020.110791 
  123. Xie, Z. and Feng, J. (2012), "Real-time nonlinear structural system identification via iterated unscented Kalman filter", Mech. Syst. Signal Process., 28, 309-322. https://doi.org/10.1016/j.ymssp.2011.02.005 
  124. Xu, Y., Brownjohn, J. and Kong, D. (2018), "A non-contact vision-based system for multipoint displacement monitoring in a cable-stayed footbridge", Struct. Control Health Monit., 25(5), e2155. https://doi.org/10.1002/stc.2155 
  125. Yang, H. and Chen, Y. (2014), "Heterogeneous recurrence monitoring and control of nonlinear stochastic processes", Chaos, 24(1), 013138. https://doi.org/10.1063/1.4869306 
  126. Yang, Y. and Nagarajaiah, S. (2013), "Blind modal identification of output-only structures in time-domain based on complexity pursuit", Earthquake Engng. Struct. Dyn., 42(13), 1885-1905. https://doi.org/10.1002/eqe.2302 
  127. Yang, Y., Dorn, C., Mancini, T., Talken, Z., Kenyon, G., Farrar, C. and Mascarenas, D. (2017a), "Blind identification of full-field vibration modes from video measurements with phase-based video motion magnification", Mech. Syst. Signal Process., 85, 567-590. https://doi.org/10.1016/j.ymssp.2016.08.041 
  128. Yang, Y., Dorn, C., Mancini, T., Talken, Z., Nagarajaiah, S., Kenyon, G., Farrar, C. and Mascarenas, D. (2017b), "Blind identification of full-field vibration modes of output-only structures from uniformly sampled, possibly temporally-aliased (sub-Nyquist), video measurements", J. Sound Vib., 390, 232-256. https://doi.org/10.1016/j.jsv.2016.11.034 
  129. Yang, Y., Dorn, C., Farrar, C. and Mascarenas, D. (2020), "Blind, simultaneous identification of full-field vibration modes and large rigid-body motion of output-only structures from digital video measurements", Eng. Struct., 207, 110183. https://doi.org/10.1016/j.engstruct.2020.110183 
  130. Ye, S.Q., Mao, X.Y., Ding, H., Ji, J.C. and Chen, L.Q. (2020), "Nonlinear vibrations of a slightly curved beam with nonlinear boundary conditions", Int. J. Mech. Sci., 168, 105294. https://doi.org/10.1016/j.ijmecsci.2019.105294 
  131. Yoon, H., Hoskere, V., Park, J.W. and Spencer, J.B. (2017), "Cross-correlation-based structural system identification using unmanned aerial vehicles", Sensors (Basel), 17(9), 2075. https://doi.org/10.3390/s17092075 
  132. Yoon, H., Shin, J. and Spencer, J.B. (2018), "Structural displacement measurement using an unmanned aerial system", Comput.-Aided Civil Infrastruct. Eng., 33(3), 183-192. https://doi.org/10.1111/mice.12338 
  133. Yuan, F.G., Zargar, S.A., Chen, Q. and Wang, S. (2020), "Machine learning for structural health monitoring: Challenges and opportunities", Sensors and smart structures technologies for civil, mechanical, and aerospace systems 2020, Vol. 11379, p. 1137903. https://doi.org/10.1117/12.2561610 
  134. Yuan, C., Chen, W., Hao, H. and Kong, Q. (2021), "Near real-time bolt-loosening detection using mask and region-based convolutional neural network", Struct. Control Health Monit., 28(7), e2741. https://doi.org/10.1002/stc.2741 
  135. Zaurin, R. and Necati, C.F. (2011), "Structural health monitoring using video stream, influence lines, and statistical analysis", Struct. Health Monit., 10(3), 309-332. https://doi.org/10.1177/1475921710373290 
  136. Zhai, W. and Peng, Z.R. (2020), "Damage assessment using google street view: Evidence from hurricane Michael in Mexico Beach Florida", Appl. Geogr., 123, 102252. https://doi.org/10.1016/j.apgeog.2020.102252 
  137. Zhang, D., Guo, J., Lei, X. and Zhu, C. (2016a), "A high-speed vision-based sensor for dynamic vibration analysis using fast motion extraction algorithms", Sensors (Basel), 16(4), 572. https://doi.org/10.3390/s16040572 
  138. Zhang, M.W., Peng, Z.K., Dong, X.J., Zhang, W.M. and Meng, G. (2016b), "Location identification of nonlinearities in MDOF systems through order determination of state-space models", Nonlinear Dyn., 84(3), 1837-1852. https://doi.org/10.1007/s11071-016-2609-4 
  139. Zhang, L., Zhou, G., Han, Y., Lin, H. and Wu, Y. (2018), "Application of internet of things technology and convolutional neural network model in bridge crack detection", IEEE Access, 6, 39442-39451. https://doi.org/10.1109/ACCESS.2018.2855144