DOI QR코드

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Damage detection of bridges based on spectral sub-band features and hybrid modeling of PCA and KPCA methods

  • 투고 : 2021.12.28
  • 심사 : 2022.04.24
  • 발행 : 2022.06.25

초록

This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T2 and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations.

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참고문헌

  1. Achilli, A., Bernagozzi, G., Betti, R., Diotallevi, P.P., Landi, L., Quqa, S. and Tronci, E.M. (2021), "On the use of multivariate autoregressive models for vibration-based damage detection and localization", Smart Struct. Syst., 27(2), 335-350. https://doi.org/10.12989/sss.2021.27.2.335.
  2. Ahmadi, H.R., Mahdavi, N. and Bayat, M. (2021), "A new index based on short time fourier transform for damage detection in bridge piers", Comput. Concrete, 27(5), 447-455. https://doi.org/10.12989/cac.2021.27.5.447.
  3. Azim, M.R., Zhang, H. and Gul, M. (2020), "Damage detection of railway bridges using operational vibration data: Theory and experimental verifications", Struct. Monit. Mainten., 7(2), 149-166. https://doi.org/10.12989/smm.2020.7.2.149.
  4. Balafas, K., Kiremidjian, A.S. and Rajagopal, R. (2018), "The wavelet transform as a Gaussian process for damage detection", Struct. Control Hlth. Monit., 25(2), e2087. https://doi.org/10.1002/stc.2087.
  5. Cao, M., Sha, G., Gao, Y. and Ostachowicz, W. (2017), "Structural damage identification using damping: a compendium of uses and features", Smart Mater. Struct., 26(4), 043001. https://doi.org/10.1088/0964-1726/26/4/043001
  6. Chatterjee, A. and Paliwal, K. (2016), "Spectral subband centroids for tone vocoder simulations of cochlear implants", Int. J. Signal Pr. Syst., 4(4), 289-294.
  7. Deng, X., Tian, X., Chen, S. and Harris, C.J. (2018), "Deep principal component analysis based on layerwise feature extraction and its application to nonlinear process monitoring", IEEE Trans. Control Syst. Technol., 27(6), 2526-2540. https://doi.org/10.1109/TCST.2018.2865413.
  8. Doebling, S.W., Farrar, C.R., Prime, M.B. and Shevitz, D.W. (1996), "Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review", Technical Report, Los Alamos National Lab., NM, United States.
  9. Esfandiari, A., Nabiyan, M.S. and Rofooei, F.R. (2020), "Structural damage detection using principal component analysis of frequency response function data", Struct. Control Hlth. Monit., 27(7), e2550. https://doi.org/10.1002/stc.2550.
  10. Ghoulem, K., Kormi, T. and Bel Hadj Ali, N. (2020), "Damage detection in nonlinear civil structures using kernel principal component analysis", Adv. Struct. Eng., 23(11), 2414-2430. https://doi.org/10.1177/1369433220913207.
  11. Gillich, G., Ntakpe, J., Wahab, M.A., Praisach, Z. and Mimis, M. (2017), "Damage detection in multi-span beams based on the analysis of frequency changes", J. Phys.: Conf. Ser., 842, 012033. https://doi.org/10.1088/1742-6596/842/1/012033
  12. Goyal, D. and Pabla, B. (2016), "The vibration monitoring methods and signal processing techniques for structural health monitoring: a review", Arch. Comput. Meth. Eng., 23(4), 585-594. https://doi.org/10.1007/s11831-015-9145-0.
  13. Gul, M. and Catbas, F.N. (2011), "Structural health monitoring and damage assessment using a novel time series analysis methodology with sensor clustering", J. Sound Vib., 330(6), 1196-1210. https://doi.org/10.1016/j.jsv.2010.09.024.
  14. Hamidian, D., Salajegheh, E. and Salajegheh, J. (2018), "Damage detection technique for irregular continuum structures using wavelet transform and fuzzy inference system optimized by particle swarm optimization", Struct. Eng. Mech., 67(5), 457-464. https://doi.org/10.12989/sem.2018.67.5.457.
  15. Kaloop, M.R. and Hu, J.W. (2015), "Stayed-cable bridge damage detection and localization based on accelerometer health monitoring measurements", Shock Vib., 2015, Article ID 102680. https://doi.org/10.1155/2015/102680.
  16. Kesavan, K.N. and Kiremidjian, A.S. (2012), "A wavelet-based damage diagnosis algorithm using principal component analysis", Struct. Control Hlth. Monit., 19(8), 672-685. https://doi.org/10.1002/stc.462.
  17. Lee, J.M., Yoo, C., Choi, S.W., Vanrolleghem, P.A. and Lee, I.B. (2004), "Nonlinear process monitoring using kernel principal component analysis", Chem. Eng. Sci., 59(1), 223-234. https://doi.org/10.1016/j.ces.2003.09.012.
  18. Li, R., Gu, H., Hu, B. and She, Z. (2019), "Multi-feature fusion and damage identification of large generator stator insulation based on lamb wave detection and SVM method", Sensor., 19(17), 3733. https://doi.org/10.3390/s19173733.
  19. Li, S., Li, H., Liu, Y., Lan, C., Zhou, W. and Ou, J. (2014), "SMC structural health monitoring benchmark problem using monitored data from an actual cable-stayed bridge", Struct. Control Hlth. Monit., 21(2), 156-172. https://doi.org/10.1002/stc.1559.
  20. Liang, Y., Li, D., Song, G. and Feng, Q. (2018), "Frequency Co-integration-based damage detection for bridges under the influence of environmental temperature variation", Measure., 125, 163-175. https://doi.org/10.1016/j.measurement.2018.04.034.
  21. Moughty, J.J. and Casas, J.R. (2017), "A state of the art review of modal-based damage detection in bridges: development, challenges, and solutions", Appl. Sci., 7(5), 510. https://doi.org/10.3390/app7050510.
  22. Nguyen, D.H., Bui, T.T., De Roeck, G. and Wahab, M.A. (2019), "Damage detection in Ca-Non Bridge using transmissibility and artificial neural networks", Struct. Eng. Mech., 71(2), 175-183. https://doi.org/10.12989/sem.2019.71.2.175.
  23. Nicolson, A., Hanson, J., Lyons, J. and Paliwal, K. (2018), "Spectral subband centroids for robust speaker identification using marginalization-based missing feature theory", Int. J. Signal Pr. Syst., 6(1), 12-16. https://doi.org/10.18178/ijsps.6.1.12-16
  24. Nie, Z., Guo, E. and Ma, H. (2019), "Structural damage detection using wavelet packet transform combining with principal component analysis", Int. J. Lifecy. Perform. Eng., 3(2), 149-170. https://doi.org/10.1504/ijlcpe.2019.100337
  25. Oliver, J.A., Kosmatka, J.B., Farrar, C.R. and Conte, J.P. (2016), "Frequency domain statistical damage identification applied to an experimental composite plate", 57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, San Diego, California, January.
  26. Pan, H., Azimi, M., Yan, F. and Lin, Z. (2018), "Time-frequency-based data-driven structural diagnosis and damage detection for cable-stayed bridges", J. Bridge Eng., 23(6), 04018033. https://doi.org/10.1061/(ASCE)BE.1943-5592.0001199.
  27. Pedram, M., Esfandiari, A. and Khedmati, M.R. (2018), "Frequency domain damage detection of plate and shell structures by finite element model updating", Invers. Prob. Sci. Eng., 26(1), 100-132. https://doi.org/10.1080/17415977.2017.1309398.
  28. Ramezani, M. and Bahar, O. (2021), "Structural damage identification for elements and connections using an improved genetic algorithm", Smart Struct. Syst., 28(5), 643-660. https://doi.org/10.12989/sss.2021.27.5.643.
  29. Razavi, M. and Hadidi, A. (2020), "Assessment of sensitivity-based FE model updating technique for damage detection in large space structures", Struct. Monit. Mainten., 7(3), 261-281. https://doi.org/10.12989/smm.2020.7.3.261.
  30. Reynders, E., Wursten, G. and De Roeck, G. (2014), "Output-only structural health monitoring in changing environmental conditions by means of nonlinear system identification", Struct. Hlth. Monit., 13(1), 82-93. https://doi.org/10.1177/1475921713502836.
  31. Sajedi, S.O. and Liang, X. (2020), "A data-driven framework for near real-time and robust damage diagnosis of building structures", Struct. Control Hlth. Monit., 27(3), e2488. https://doi.org/10.1002/stc.2488.
  32. Santos, A., Silva, M., Sales, C., Costa, J. and Figueiredo, E. (2015), "Applicability of linear and nonlinear principal component analysis for damage detection", 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, May.
  33. Shokrani, Y., Dertimanis, V.K., Chatzi, E.N. and N. Savoia, M. (2018), "On the use of mode shape curvatures for damage localization under varying environmental conditions", Struct. Control Hlth. Monit., 25(4), e2132. https://doi.org/10.1002/stc.2132.
  34. Shyamala, P., Mondal, S. and Chakraborty, S. (2018), "Numerical and experimental investigation for damage detection in FRP composite plates using support vector machine algorithm", Struct. Monit. Mainten., 5(2), 243-260. https://doi.org/10.12989/smm.2018.5.2.243.
  35. Sohn, H., Worden, K. and Farrar, C.R. (2002), "Statistical damage classification under changing environmental and operational conditions", J. Intel. Mater. Syst. Struct., 13(9), 561-574. https://doi.org/10.1106/104538902030904.
  36. Soo Lon Wah, W., Chen, Y.T., Roberts, G.W. and Elamin, A. (2018), "Separating damage from environmental effects affecting civil structures for near real-time damage detection", Struct. Hlth. Monit., 17(4), 850-868. https://doi.org/10.1177/1475921717722060.
  37. Sousa Tome, E., Pimentel, M. and Figueiras, J. (2019), "Online early damage detection and localisation using multivariate data analysis: Application to a cable-stayed bridge", Struct. Control Hlth. Monit., 26(11), e2434. https://doi.org/10.1002/stc.2434.
  38. Xin, Y., Hao, H. and Li, J. (2019), "Operational modal identification of structures based on improved empirical wavelet transform", Struct. Control Hlth. Monit., 26(3), e2323. https://doi.org/10.1002/stc.2323.
  39. Yin, T. and Zhu, H.P. (2018), "Probabilistic damage detection of a steel truss bridge model by optimally designed Bayesian neural network", Sensor., 18(10), 3371. https://doi.org/10.3390/s18103371.
  40. Zhang, J. and Aoki, T. (2019), "A frequency-domain noniterative algorithm for structural parameter identification of shear buildings subjected to frequent earthquakes", Comput.-Aid. Civil Infrastr. Eng., 35(6), 615-627. https://doi.org/10.1111/mice.12502.
  41. Zhang, Z., Sun, C., Li, C. and Sun, M. (2019), "Vibration based bridge scour evaluation: A data-driven method using support vector machines", Struct. Monit. Mainten., 6(2), 125-145. https://doi.org/10.12989/smm.2019.6.2.125.