Smart Structures and Systems

  • 제29권1호
  • /
  • Pages.41-51
  • /
  • 2022
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Unsupervised one-class classification for condition assessment of bridge cables using Bayesian factor analysis

  • Wang, Xiaoyou (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Li, Lingfang (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Tian, Wei (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Du, Yao (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Hou, Rongrong (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University) ;
  • Xia, Yong (Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University)
  • 투고 : 2021.04.02
  • 심사 : 2021.07.28
  • 발행 : 2022.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

Cables are critical components of cable-stayed bridges. A structural health monitoring system provides real-time cable tension recording for cable health monitoring. However, the measurement data involve multiple sources of variability, i.e., varying environmental and operational factors, which increase the complexity of cable condition monitoring. In this study, a one-class classification method is developed for cable condition assessment using Bayesian factor analysis (FA). The single-peaked vehicle-induced cable tension is assumed to be relevant to vehicle positions and weights. The Bayesian FA is adopted to establish the correlation model between cable tensions and vehicles. Vehicle weights are assumed to be latent variables and the influences of different transverse positions are quantified by coefficient parameters. The Bayesian theorem is employed to estimate the parameters and variables automatically, and the damage index is defined on the basis of the well-trained model. The proposed method is applied to one cable-stayed bridge for cable damage detection. Significant deviations of the damage indices of Cable SJS11 were observed, indicating a damaged condition in 2011. This study develops a novel method to evaluate the health condition of individual cable using the FA in the Bayesian framework. Only vehicle-induced cable tensions are used and there is no need to monitor the vehicles. The entire process, including the data pre-processing, model training and damage index calculation of one cable, takes only 35 s, which is highly efficient.

키워드

과제정보

The authors are grateful to the organisers of the 1st International Project Competition for SHM (IPC-SHM, 2020) for generously providing the excellent opportunities during the COVID-19 and invaluable data from the actual structures. Our gratitude goes to Professor Hui Li and Professor Billie F. Spencer Jr., Chairs of IPC-SHM, 2020. This research is also supported by the Key-Area Research and Development Program of Guangdong Province (Project No. 2019B111106001) and Research Grants Council of HKSAR-General Research Fund (Project No. 15201920).

참고문헌

  1. Bartholomew, D.J., Knott, M. and Moustaki, I. (2011), Latent Variable Models and Factor Analysis: A Unified Approach, John Wiley & Sons.
  2. Beck, J.L. and Katafygiotis, L.S. (1998), "Updating models and their uncertainties. I: Bayesian statistical framework", J. Eng. Mech., 124(4), 455-461. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:4(455)
  3. Bishop, C.M. (1999), "Bayesian PCA", Advances in Neural Information Processing Systems, pp. 382-388.
  4. Bishop, C.M. (2006), Pattern Recognition and Machine Learning, New York, Springer.
  5. Brownjohn, J.M., Stefano, A.D., Xu, Y.-L., Wenzel, H. and Aktan, A.E. (2011), "Vibration-based monitoring of civil infrastructure: challenges and successes", J. Civil Struct. Health Monitor., 1, 79-95. https://doi.org/10.1007/s13349-011-0009-5
  6. Fan, Z.Y., Huang, Q., Ren, Y., Zhu, Z.Y. and Xu, X. (2020), "A cointegration approach for cable anomaly warning based on structural health monitoring data: An application to cable-stayed bridges", Adv. Struct. Eng., 23(13), 2789-2802. https://doi.org/10.1177/1369433220924793
  7. Farrar, C.R. and Worden, K. (2012), Structural Health Monitoring: A Machine Learning Perspective, John Wiley & Sons.
  8. Figueiredo, E., Park, G., Farrar, C.R., Worden, K. and Figueiras, J. (2011), "Machine learning algorithms for damage detection under operational and environmental variability", Struct. Health Monitor., 10(6), 559-572. https://doi.org/10.1177/1475921710388971
  9. Guo, W.H. and Xu, Y.L. (2001), "Fully computerized approach to study cable-stayed bridge-vehicle interaction", J. Sound Vib., 248(4), 745-761. https://doi.org/10.1006/jsvi.2001.3828
  10. Hoffmann, H. (2007), "Kernel PCA for novelty detection", Pattern Recogn., 40(3), 863-874. https://doi.org/10.1016/j.patcog.2006.07.009
  11. Huang, Y., Shao, C., Wu, B., Beck, J.L. and Li, H. (2019), "State-of-the-art review on Bayesian inference in structural system identification and damage assessment", Adv. Struct. Eng., 22(6), 1329-1351. https://doi.org/10.1177/1369433218811540
  12. Jing, H., Xia, Y., Li, H., Xu, Y. and Li, Y. (2017), "Excitation mechanism of rain-wind induced cable vibration in a wind tunnel", J. Fluids Struct., 68, 32-47. https://doi.org/10.1016/j.jfluidstructs.2016.10.006
  13. Ko, J. and Ni, Y.Q. (2005), "Technology developments in structural health monitoring of large-scale bridges", Eng. Struct., 27(12), 1715-1725. https://doi.org/10.1016/j.engstruct.2005.02.021
  14. Li, H. and Ou, J. (2016), "The state of the art in structural health monitoring of cable-stayed bridges", J. Civil Struct. Health Monitor., 6(1), 43-67. https://doi.org/10.1007/s13349-015-0115-x
  15. Li, H., Li, S., Ou, J. and Li, H. (2010), "Modal identification of bridges under varying environmental conditions: temperature and wind effects", Struct. Control Health Monitor., 17(5), 495-512. https://doi.org/10.1002/stc.319
  16. Li, S., Wei, S., Bao, Y. and Li, H. (2018), "Condition assessment of cables by pattern recognition of vehicle-induced cable tension ratio", Eng. Struct., 155, 1-15. https://doi.org/10.1016/j.engstruct.2017.09.063
  17. Markou, M. and Singh, S. (2003a), "Novelty detection: a review-Part 1: Statistical approaches", Signal Process., 83(12), 2481-2497. https://doi.org/10.1016/j.sigpro.2003.07.018
  18. Markou, M. and Singh, S. (2003b), "Novelty detection: a review-Part 2: Neural network based approaches", Signal Process., 83(12), 2499-2521. https://doi.org/10.1016/j.sigpro.2003.07.019
  19. Murphy, K.P. (2012), Machine Learning: A Probabilistic Perspective, Cambridge: MIT Press, Cambridge, UK.
  20. Ni, Y.Q., Hua, X.G., Fan, K.Q. and Ko, J.M. (2005), "Correlating modal properties with temperature using long-term monitoring data and support vector machine technique", Eng. Struct., 27(12), 1762-1773. https://doi.org/10.1016/j.engstruct.2005.02.020
  21. Pimentel, M.A., Clifton, D.A., Clifton, L. and Tarassenko, L. (2014), "A review of novelty detection", Signal Process., 99, 215-249. https://doi.org/10.1016/j.sigpro.2013.12.026
  22. 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. Health Monitor., 13(1), 82-93. https://doi.org/10.1177/1475921713502836
  23. Scholkopf, B., Williamson, R.C., Smola, A.J., Shawe Taylor, J. and Platt, J.C. (1999), Support vector method for novelty detection, NIPS, Citeseer.
  24. Sen, D., Erazo, K., Zhang, W., Nagarajaiah, S. and Sun, L. (2019), "On the effectiveness of principal component analysis for decoupling structural damage and environmental effects in bridge structures", J. Sound Vib., 457, 280-298. https://doi.org/10.1016/j.jsv.2019.06.003
  25. Sohn, H., Worden, K. and Farrar, C.R. (2001), "Novelty detection under changing environmental conditions", Smart Structures and Materials 2001: Smart Systems for Bridges, Structures, and Highways, International Society for Optics and Photonics.
  26. Sun, L., Shang, Z., Xia, Y., Bhowmick, S. and Nagarajaiah, S. (2020), "Review of bridge structural health monitoring aided by big data and artificial intelligence: from condition assessment to damage detection", J. Struct. Eng., 146(5), 04020073. https://doi.org/10.1061/(ASCE)ST.1943-541X.0002535
  27. Tipping, M.E. (2001), "Sparse Bayesian learning and the relevance vector machine", J. Mach. Learn. Res., 1, 211-244. https://doi.org/10.1162/15324430152748236
  28. Wang, X., Hou, R., Xia, Y. and Zhou, X. (2020), "Structural damage detection based on variational Bayesian inference and delayed rejection adaptive Metropolis algorithm", Struct. Health Monitor., 1475921720921256. https://doi.org/10.1177/1475921720921256
  29. Wang, X., Li, L., Beck, J.L. and Xia, Y. (2021), "Sparse Bayesian factor analysis for structural damage detection under unknown environmental conditions", Mech. Syst. Signal Process., 154, 107563. https://doi.org/0.1016/j.ymssp.2020.107563 https://doi.org/10.1016/j.ymssp.2020.107563
  30. Xu, Y.L. and Xia, Y. (2011). Structural Health Monitoring of Long-Span Suspension Bridges, London: CRC Press, London, UK.
  31. Yan, A.M., Kerschen, G., De Boe, P. and Golinval, J.C. (2005a), "Structural damage diagnosis under varying environmental conditions-Part I: A linear analysis", Mech. Syst. Signal Process., 19(4), 847-864. https://doi.org/10.1016/j.ymssp.2004.12.002
  32. Yan, A.M., Kerschen, G., De Boe, P. and Golinval, J.C. (2005b), "Structural damage diagnosis under varying environmental conditions-Part II: Local PCA for non-linear cases", Mech. Syst. Signal Process., 19(4), 865-880. https://doi.org/10.1016/j.ymssp.2004.12.003