Smart Structures and Systems

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Long term structural health monitoring for old deteriorated bridges: a copula-ARMA approach

  • Zhang, Yi (School of Civil Engineering, Tsinghua University) ;
  • Kim, Chul-Woo (Department of Civil and Earth Resources Engineering, Kyoto University) ;
  • Zhang, Lian (Department of Civil and Earth Resources Engineering, Kyoto University) ;
  • Bai, Yongtao (College of Civil Engineering, Chongqing University) ;
  • Yang, Hao (Geodetic Institute, Faculty of Civil Engineering and Geodetic Science, Leibniz University Hanover) ;
  • Xu, Xiangyang (Geodetic Institute, Faculty of Civil Engineering and Geodetic Science, Leibniz University Hanover) ;
  • Zhang, Zhenhao (School of Civil Engineering, Changsha University of Science and Technology)
  • Received : 2018.11.12
  • Accepted : 2019.12.26
  • Published : 2020.03.25
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Abstract

Long term structural health monitoring has gained wide attention among civil engineers in recent years due to the scale and severity of infrastructure deterioration. Establishing effective damage indicators and proposing enhanced monitoring methods are of great interests to the engineering practices. In the case of bridge health monitoring, long term structural vibration measurement has been acknowledged to be quite useful and utilized in the planning of maintenance works. Previous researches are majorly concentrated on linear time series models for the measurement, whereas nonlinear dependences among the measurement are not carefully considered. In this paper, a new bridge health monitoring method is proposed based on the use of long term vibration measurement. A combination of the fundamental ARMA model and copula theory is investigated for the first time in detecting bridge structural damages. The concept is applied to a real engineering practice in Japan. The efficiency and accuracy of the copula based damage indicator is analyzed and compared in different window sizes. The performance of the copula based indicator is discussed based on the damage detection rate between the intact structural condition and the damaged structural condition.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China

The authors gratefully acknowledge the financial support from National Natural Science Foundation of China under project number of Grand No. 51908324. The support from Tsinghua University Initiative Scientific Research Program is also greatly appreciated.

References

  1. Akaike, H. (1974), "A new look at the statistical model identification", IEEE Transact. Auto. Control, 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
  2. Chang, K.C. and Kim, C.W. (2016), "Modal-parameter identification and vibration-based damage detection of a damaged steel truss bridge", Eng. Struct., 122, 156-173. https://doi.org/10.1016/j.engstruct.2016.04.057
  3. Cho, S., Jo, H., Jang, S., Park, J., Jung, H.J., Yun, C.B. and Seo, J.W. (2010), "Structural health monitoring of a cable-stayed bridge using wireless smart sensor technology: data analyses", Smart Struct. Syst., Int. J., 6(5-6), 461-480. https://doi.org/10.12989/sss.2010.6.5_6.461
  4. Deraemaeker, A., Reynders, E., De Roeck, G. and Kullaa, J. (2007), "Vibration-based structural health monitoring using output-only measurements under changing environment", Mech. Syst. Signal Process., 22(1), 34-56. https://doi.org/10.1016/j.ymssp.2007.07.004
  5. Dilena, M. and Morassi, A. (2011), "Dynamic testing of damaged bridge", Mech. Syst. Signal Process., 25, 1485-1507. https://doi.org/10.1016/j.ymssp.2010.12.017
  6. 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", Los Alamos National Laboratory Report, LA-3070-MS.
  7. Dohler, M., Hille, F., Mevel, L. and Rucker, W. (2012), "Structural health monitoring with statistical methods during progressive damage test of S101 Bridge", Eng. Struct., 69, 183-193. https://doi.org/10.1016/j.engstruct.2014.03.010
  8. Garg, A., Hazra, B., Zhu, H. and Wen, Y. (2019), "A simplified probabilistic analysis of water content and wilting in soil vegetated with non-crop species", Catena, 175, 123-131. https://doi.org/10.1016/j.catena.2018.12.016
  9. Gopal, P., Bordoloi, S., Ratnam, R., Lin, P., Cai, W., Buragohain, P., Garg, A. and Sreedeep, S. (2019), "Investigation of infiltration rate for soil-biochar composites of water hyacinth", Acta Geophysica, 67(1), 231-246. https://doi.org/10.1007/s11600-018-0237-8
  10. Gul, M. and Catbas, F.N. (2009), "Statistical pattern recognition for Structural Health Monitoring using time series modeling: Theory and experimental verifications", Mech. Syst. Signal Process., 23(7), 2192-2204. https://doi.org/10.1016/j.ymssp.2009.02.013
  11. Huynh, T.C., Dang, N.L. and Kim, J.T. (2018), "PCA-based filtering of temperature effect on impedance monitoring in prestressed tendon anchorage", Smart Struct. Syst., Int. J., 22(1), 57-70. https://doi.org/10.12989/sss.2018.22.1.057
  12. Kim, C.W., Kawatani, M. and Hao, J. (2012), "Modal parameter identification of short span bridges under a moving vehicle by means of multivariate AR model", Struct. Infrastruct. Eng., 8(5), 459-472. https://doi.org/10.1080/15732479.2010.539061
  13. Kim, C.W., Isemoto, R., Sugiura, K. and Kawatani, M. (2013), "Structural fault detection of bridges based on linear system parameter and MTS method", J. JSCE, 1(1), 32-43. https://doi.org/10.2208/journalofjsce.1.1_32
  14. Ko, J.M. and Ni, Y.Q. (2005), "Technology developments in structural health monitoring of large-scale brides", Eng. Struct., 27(12), 1715-1725. https://doi.org/10.1016/j.engstruct.2005.02.021
  15. Liebscher, E. (2008), "Construction of asymmetric multivariate copulas", J. Multivar. Anal., 99(10), 2234-2250. https://doi.org/10.1016/j.jmva.2008.02.025
  16. Magalhaes, F., Cunha, A. and Caetano, E. (2010), "Continuous dynamic monitoring of an arch bridge: strategy to eliminate the environmental and operational effects and detect damages", Proceedings of the 24th International Conference on Noise and Vibration engineering (ISMA2010); Organised in conjunction with the 3rd International Conference on Uncertainty in Structural Dynamics (USD2010), Leuven, Belgium, September.
  17. Nair, K.K., Kiremidjian, A.S. and Law, K.H. (2006), "Time seriesbased damage detection and localization algorithm with application to the ASCE benchmark structure", Sound Vib., 291, 349-368. https://doi.org/10.1016/j.jsv.2005.06.016
  18. Nelsen, R.B. (2006), An Introduction to Copulas, Springer, New York, USA.
  19. Reynders, E., Wursten, G. and De Roeck, G. (2013), "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
  20. Salawu, O.S. (1997), "Detection of structural damage through changes in frequency: A review", Eng. Struct., 19, 718-723. https://doi.org/10.1016/S0141-0296(96)00149-6
  21. Salvadori, G. and De Michele, C. (2007), "On the use of copulas in hydrology: theory and practice", J. Hydrol. Eng., 12(4), 369-380. https://doi.org/10.1061/(ASCE)1084-0699(2007)12:4(369)
  22. Spiridonakos, M. and Chatzi, E. (2014), "Stochastic structural identification from vibrational and environmental data", In: Encyclopedia of Earthquake Engineering, (M. Beer, E. Patelli, I. Kougioumtzoglou, I. Au Ed.), Springer-Verlag Berlin Heidelberg.
  23. Spiridonakos, M., Chatzi, E. and Sudret, B. (2016), "Polynomial chaos expansion models for the monitoring of structures under operational variability", ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civil Eng., 2(3), B4016003. https://doi.org/10.1061/AJRUA6.0000872
  24. Wang, H., Tao, T., Li, A. and Zhang, Y. (2016), "Structural health monitoring system for Sutong cable-stayed bridge", Smart Struct. Syst., Int. J., 18(2), 317-334. https://doi.org/10.12989/sss.2016.18.2.317
  25. Worden, K., Manson, G. and Fieller, N.R.J. (2000), "Damage detection using outlier analysis", J. Sound Vib., 29(3), 647-667. https://doi.org/10.1006/jsvi.1999.2514
  26. Xia, Y.X. and Ni, Y.Q. (2016), "Extrapolation of extreme traffic load effects on bridges based on long-term SHM data", Smart Struct. Syst., Int. J., 17(6), 995-1015. https://doi.org/10.12989/sss.2016.17.6.995
  27. Xu, X., Yang, H., Augello, R. and Carrera, E. (2019a), "Optimized free-form surface modeling of point clouds from laser-based measurement", Mech. Adv. Mater. Struct. https://doi.org/10.1080/15376494.2019.1688435
  28. Xu, X., Augello, R. and Yang, H. (2019b), "The generation and validation of a CUF-based FEA model with laser-based experiments", Mech. Adv. Mater. Struct. https://doi.org/10.1080/15376494.2019.1697473
  29. Yang, Y., Huang, H. and Sun, L. (2017), "Numerical studies on the effect of measurement noises on the online parametric identification of a cable-stayed bridge", Smart Struct. Syst., Int. J., 19(3), 259-268. https://doi.org/10.12989/sss.2017.19.3.259
  30. Zhang, Y., Beer, M. and Quek, S.T. (2015), "Long-term performance assessment and design of offshore structures", Comput. Struct., 154, 101-115. https://doi.org/10.1016/j.compstruc.2015.02.029
  31. Zhang, Y., Kim, C.W., Tee, K.F. and Lam, J.S.L. (2017a), "Optimal sustainable life cycle maintenance strategies for port infrastructures", J. Cleaner Production, 142, 1693-1709. https://doi.org/10.1016/j.jclepro.2016.11.120
  32. Zhang, Y., Kim, C.W. and Tee, K.F. (2017b), "Maintenance management of offshore structures using Markov process model with random transition probabilities", Struct. Infrastruct. Eng., 13(8), 1068-1080. https://doi.org/10.1080/15732479.2016.1236393
  33. Zhang, Y., Kim, C.W., Tee, K.F., Garg, A. and Garg, A. (2018a), "Long-term health monitoring for deteriorated bridge structures based on copula theory", Smart Struct. Syst., Int. J., 21(2), 171-185. https://doi.org/10.12989/sss.2018.21.2.171
  34. Zhang, Y., Kim, C.W., Beer, M., Dai, H. and Guedes Soares, C. (2018b), "Modeling multivariate ocean data using asymmetric copulas", Coastal Eng., 135, 91-111. https://doi.org/10.1016/j.coastaleng.2018.01.008
  35. Zhang, Y. and Lam, J.S.L. (2015a), "Estimating the economic losses of port disruption due to extreme wind events", Ocean Coastal Manag., 116, 300-310. https://doi.org/10.1016/j.ocecoaman.2015.08.009
  36. Zhang, Y. and Lam, J.S.L. (2015b), "Reliability analysis of offshore structures within a time varying environment", Stochastic Environ. Res. Risk Assess., 29(6), 1615-1636. https://doi.org/10.1007/s00477-015-1084-7
  37. Zhang, Y. and Lam, J.S.L. (2016), "A copula approach in the point estimate method for reliability engineering", Quality Reliabil. Eng. Int., 32, 1501-1508. https://doi.org/10.1002/qre.1860
  38. Zhang, Y., Gomes, A., Beer, M., Neumann, I. and Nackenhorst, U. (2019), "Reliability analysis with consideration of asymmetrically dependent variables: discussion and application to geotechnical examples", Reliabil. Eng. Syst. Safe., 185, 261-277. https://doi.org/10.1016/j.ress.2018.12.025