Smart Structures and Systems

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Damage detection of bridge structures under unknown seismic excitations using support vector machine based on transmissibility function and wavelet packet energy

  • Liu, Lijun (School of Architecture and Civil Engineering, Xiamen University) ;
  • Mi, Jianan (School of Architecture and Civil Engineering, Xiamen University) ;
  • Zhang, Yixiao (School of Architecture and Civil Engineering, Xiamen University) ;
  • Lei, Ying (School of Architecture and Civil Engineering, Xiamen University)
  • Received : 2020.08.16
  • Accepted : 2020.12.10
  • Published : 2021.02.25
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Abstract

Since it may be hard to obtain the exact external load in practice, damage identification of bridge structures using only structural responses under unknown seismic excitations is an important but challenging task. Since structural responses are determined by both structural properties and seismic excitation, it is necessary to remove the effects of external excitation and only retain the structural information for structural damage identification. In this paper, a data-driven approach using structural responses only is proposed for structural damage alarming and localization of bridge structures. The transmissibility functions (TF) of structural responses are used to eliminate the influence of unknown seismic excitations. Moreover, the inverse Fourier transform of TFs and wavelet packet transform are used to reduce the influence of frequency bands and to extract the damage-sensitive feature, respectively. Based on Support vector machines (SVM), structural responses under ambient excitations are used for training SVM. Then, structural responses under unknown seismic excitations are also processed accordingly and used for damage alarming and localization by the trained SMV. The numerical simulation examples of beam-type bridge and a cable-stayed bridge under unknown seismic excitations are studied to illustrate the performance of the proposed approach.

Keywords

Acknowledgement

The research in this paper is supported by the National Key R&D Program of China via Grant No. 2017YFC1500603. The authors thank Associate Prof. Ye Xia at Tongji University for providing the benchmark model of Haiwen bridge.

References

  1. An, Y.H., Spencer, B.F. and Ou, J.P. (2015), "A Test method for damage diagnosis of suspension bridge suspender cables", Comput.-Aided Civil Infrastruct. Eng., 30(10), 771-784. https://doi.org/10.1111/mice.12144
  2. An, Y.H., Chatzi, E., Sim, S.H., Laflamme, S., Blachowski, B. and Ou, J.P. (2019), "Recent progress and future trends on damage identification methods for bridge structures", Struct. Control Health Monitor., 26(10), e2416. https://doi.org/10.1002/stc.2416
  3. Bao, Y. and Li, H. (2020), "Machine learning paradigm for structural health monitoring", Struct. Health Monitor. https://doi. org/10.1177/1475921720972416
  4. Bao, Y.Q., Chen, Z., Wei, S., Xu, Y., Tang, Z. and Li, H. (2019), "The state of the art of data science and engineering in structural health monitoring", Eng., 5(2), 234-242. https://doi.org/10.1016/j.eng.2018.11.027
  5. Chang, C.C. and Lin, C.J. (2001), "LIBSVM: a library for support vector machines". http://www.csie.ntu.edu.tw/cjlin/papers/guide/data
  6. Cheng, L., Busca, G., Roberto, P., Vanali, M. and Cigada, A. (2017), "Damage Detection Based on Strain Transmissibility for Beam Structure by Using Distributed Fiber Optics", Proceedings of the Society for Experimental Mechanics Series, Volume 7, pp. 27-40. https://doi.org/10.1007/978-3-319-54109-9_4
  7. Cherkassky, V. (1997), "The nature of statistical learning theory", IEEE Transact. Neural Networks, 8(6), 1564-1564. https://doi.org/10.1007/978-1-4757-2440-0
  8. Chesne, S. and Deraemaeker, A. (2013), "Damage localization using transmissibility functions: A critical review", Mech. Syst. Signal Process., 38(2), 569-584. https://doi.org/10.1016/j.ymssp.2013.01.020
  9. Cortes, C. and Vapnik, V. (1995), "Support-vector networks", Mach. Learn, 20(3), 273-297. https://doi.org/10.1023/A:1022627411411
  10. Diao, Y., Men, X., Sun, Z., Guo, K. and Wang, Y. (2018), "Structural Damage Identification Based on the Transmissibility Function and Support Vector Machine", Shock Vib., 1-13. https://doi.org/10.1155/2018/4892428
  11. Dushyanth, N.D., Suma, M.N. and Latte, M.V. (2016), "Detection and localization of damage using empirical mode decomposition and multilevel support vector machine", Appl. Phys. A, 122(3). https://doi.org/10.1007/s00339-016-9753-z
  12. Fan, Z., Feng, X. and Zhou, J. (2013), "A novel transmissibility concept based on wavelet transform for structural damage detection", Smart Struct. Syst., Int. J., 12(3), 291-308. https://doi.org/10.12989/sss.2013.12.3_4.291
  13. Fujino, Y., Kikkawa, H., Namikawa, K. and Mizoguchi, T. (2005), "Seismic retrofit design of long-span bridges on metropolitan expressways in Tokyo", Transport. Res. Record: J. Transport. Res. Board, 11, 335-342. https://doi.org/10.3141/trr.11s.1371l823v02811m0
  14. Gui, G., Pan, H., Lin, Z., Li, Y. and Yuan, Z. (2017), "Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection", KSCE J. Civil Eng., 21(2), 523-534. https://doi.org/10.1007/s12205-017-1518-5
  15. Huang, H. and Wu, Z.S. (2017), "Monitoring and structural analysis of a rehabilitated box girder bridge based on long-gauge strain sensors", Struct. Health Monitor., 17(3), 586-597. https://doi:10.1016/j.engstruct.2015.04.024
  16. Jiang, Z.G. and Chen, B. (2012), "Performance Degradation Assessment of a Beam Structure by Using Wavelet Packet Energy", Appl. Mech. Mater., 166-169, 1102-1107. https://doi.org/10.4028/www.scientific.net/AMM.166-169.1102
  17. Kaloop, M.R. and Li, H. (2011), "Sensitivity and analysis GPS signals based bridge damage using GPS observations and wavelet transform", Measurement, 44(5), 927-937. https://doi.org/10.1016/j.measurement.2011.02.008
  18. Li, H., Tao, D.W., Huang, Y. and Bao, Y.Q. (2013), "A data-driven approach for seismic damage detection of shear-type building structures using the fractal dimension of time-frequency features", Struct. Control Health Monitor., 20(9), 1191-1210. https://doi.org/10.1002/stc.1528
  19. Li, X.Z., Peng, Z.K., Dong, X.J., Zhang, W.M. and Meng, G. (2015), "A new transmissibility based indicator of local variation in structure and its application for damage detection", Shock Vib., 1-18. https://doi.org/10.1155/2015/850286
  20. Liu, X., Lieven, N.A.J. and Escamilla-Ambrosio, P.J. (2009), "Frequency response function shape-based methods for structural damage localization", Mech. Syst. Signal Process., 23(4), 1243-1259. https://doi.org/10.1016/j.ymssp.2008.10.002
  21. Luts, J., Molenberghs, G., Verbeke, G., Van Huffel, S. and Suykens, J.A.K. (2012), "A mixed effects least squares support vector machine model for classification of longitudinal data", Computat. Statist. Data Anal., 56(3), 611-628. https://doi.org/10.1016/j.csda.2011.09.008
  22. Maia, N.M.M., Silva, J.M.M., Almas, E.A.M. and Sampaio, R.P.C. (2003), "Damage detection in structures: from mode shape to frequency response function methods", Mech. Syst. Signal Process., 17(3), 489-498. https://doi.org/10.1006/mssp.2002.1506
  23. Maia, N.M.M., Ribeiro, A.M.R., Fontul, M., Montalvao, D. and Sampaio, R.P.C. (2007), "Using the detection and relative damage quantification indicator (DRQ) with transmissibility", Key Eng. Mater., 347, 455-460. https://doi.org/10.4028/www.scientific.net/KEM.347.455
  24. Maia, N.M.M., Almeida, R.A.B., Urgueira, A.P.V. and Sampaio, R.P.C. (2011), "Damage detection and quantification using transmissibility", Mech. Syst. Signal Process., 25(7), 2475-2483. https://doi.org/10.1016/j.ymssp.2011.04.002
  25. Noori, M., Wang, H., Altabey, W.A. and Silik, A.I. (2018), "A modified wavelet energy rate-based damage identification method for steel bridges", Scientia Iranica, 25, 3210-3230. https://doi.org/10.24200/sci.2018.20736
  26. Ren, W.-X., Sun, Z.-S., Xia, Y., Hao, H. and Deeks, A.J. (2008), "Damage identification of shear connectors with wavelet packet energy: laboratory test study", J. Struct. Eng., 134(5), 832-841. https://doi.org/10.1061/(ASCE)0733-9445(2008)134: 5(832)
  27. Siringoringo, D.M. and Fujino, Y. (2006), "Experimental study of laser Doppler vibrometer and ambient vibration for vibration-based damage detection", Eng. Struct., 28(13), 1803-1815. https://doi.org/10.1016/j.engstruct.2006.03.006
  28. Sun, Z. and Chang, C.C. (2002), "Structural Damage Assessment Based on Wavelet Packet Transform", J. Struct. Eng., 128(10), 1354-1361. https://doi.org/10.1061/(ASCE)0733-9445(2002)12 8:10(1354)
  29. Tjen, J., Francesco, S. and Alessandro, D. (2020), "An entropy-based sensor selection approach for structural damage detection", Proceedings of 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE).
  30. Wang, P. and Shi, Q. (2018), "Damage Identification in Structures Based on Energy Curvature Difference of Wavelet Packet Transform", Shock Vib., 1-13. https://doi.org/10.1155/2018/4830391
  31. Xu, Y.L., Zhang, C.D., Zhan, S. and Spencer, B.F. (2018), "Multi-level damage identification of a bridge structure: a combined numerical and experimental investigation", Eng. Struct., 156, 53-67. https://doi.org/10.1016/j.engstruct.2017.11.014
  32. Yan, P. and Li, Q. (2012), "Structural Damage Detection of Continuous Beam by NExT Based Wavelet Packet Energy", Adv. Mater. Res., 490-495, 2588-2593. https://doi.org/10.4028/www.scientific.net/AMR.490-495.2588
  33. Yan, W.-J., Zhao, M.-Y., Sun, Q. and Ren, W.-X. (2019), "Transmissibility-based system identification for structural health Monitoring: Fundamentals, approaches, and applications", Mech. Syst. Signal Process., 117, 453-482. https://doi.org/10.1016/j.ymssp.2018.06.053
  34. Young Noh, H., Krishnan Nair, K., Lignos, D.G. and Kiremidjian, A.S. (2011), "Use of wavelet-based damage-sensitive features for structural damage diagnosis using strong motion data", J. Struct. Eng., 137(10), 1215-1228. https://doi.org/10.1061/(ASCE)ST.1943-541X.0000385
  35. Zhou, Y. and Wahab, M.A. (2016), "Rapid early damage detection using transmissibility with distance measure analysis under unknown excitation in long-term health monitoring", J. Vibroeng., 20(1), 823-831. https://doi.org/10.21595/jve.2016.19718
  36. Zhou, Y.-L. and Wahab, M.A. (2017), "Cosine based and extended transmissibility damage indicators for structural damage detection", Eng. Struct., 141, 175-183. https://doi.org/10.1016/j.engstruct.2017.03.030
  37. Zhou, Y.-L., Figueiredo, E., Maia, N., Sampaio, R. and Perera, R. (2015), "Damage detection in structures using a transmissibility-based Mahalanobis distance", Struct. Control Health Monitor., 22(10), 1209-1222. https://doi.org/10.1002/stc.1743
  38. Zhou, Y.-L., Maia, N.M. and Abdel Wahab, M. (2016), "Damage detection using transmissibility compressed by principal component analysis enhanced with distance measure", J. Vib. Control, 24(10), 2001-2019. https://doi.org/10.1177/1077546316674544
  39. Zhu, D., Yi, X. and Wang, Y. (2015), "A local excitation and measurement approach for decentralized damage detection using transmissibility functions", Struct. Control Health Monitor., 23(3), 487*502. https://doi.org/10.1002/stc.1781