Smart Structures and Systems

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

A Bayesian approach for vibration-based long-term bridge monitoring to consider environmental and operational changes

  • Kim, Chul-Woo (Department of Civil and Earth Resources Engineering, Graduate School of Engineering, Kyoto University) ;
  • Morita, Tomoaki (Department of Civil and Earth Resources Engineering, Graduate School of Engineering, Kyoto University) ;
  • Oshima, Yoshinobu (Department of Civil and Earth Resources Engineering, Graduate School of Engineering, Kyoto University) ;
  • Sugiura, Kunitomo (Department of Civil and Earth Resources Engineering, Graduate School of Engineering, Kyoto University)
  • Received : 2014.12.05
  • Accepted : 2015.01.11
  • Published : 2015.02.25
⟨ Previous Next ⟩

Abstract

This study aims to propose a Bayesian approach to consider changes in temperature and vehicle weight as environmental and operational factors for vibration-based long-term bridge health monitoring. The Bayesian approach consists of three steps: step 1 is to identify damage-sensitive features from coefficients of the auto-regressive model utilizing bridge accelerations; step 2 is to perform a regression analysis of the damage-sensitive features to consider environmental and operational changes by means of the Bayesian regression; and step 3 is to make a decision on the bridge health condition based on residuals, differences between the observed and predicted damage-sensitive features, utilizing 95% confidence interval and the Bayesian hypothesis testing. Feasibility of the proposed approach is examined utilizing monitoring data on an in-service bridge recorded over a one-year period. Observations through the study demonstrated that the Bayesian regression considering environmental and operational changes led to more accurate results than that without considering environmental and operational changes. The Bayesian hypothesis testing utilizing data from the healthy bridge, the damage probability of the bridge was judged as no damage.

Keywords

Acknowledgement

Supported by : JSPS

References

  1. Akaike, H. (1974), "A new look at the statistical model identification", IEEE T. Autom. Contr., 19(6), 716-723. https://doi.org/10.1109/TAC.1974.1100705
  2. Cunha, A., Caetano, E., Magalhaes, F. and Moutinho, C. (2013), "Recent perspectives in dynamic testing and monitoring of bridges", Struct. Control Health Monit., 20(6), 853-877. https://doi.org/10.1002/stc.1516
  3. 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 Pr., 22(1), 34-56. https://doi.org/10.1016/j.ymssp.2007.07.004
  4. Dilena, M. and Morassi, A. (2011), "Dynamic testing of damaged bridge", Mech. Syst. Signal Pr., 25, 1485-1507. https://doi.org/10.1016/j.ymssp.2010.12.017
  5. 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.
  6. Heng, S., Oshima, Y. and Kawano, H. (2011), "One Year Monitoring of Bridge Frequency and Traffic Load on a Road Bridge", Proceedings of the 24th KKCNN Symposium on Civil Engineering.
  7. Jeffreys, H. (1998), Theory of Probability, Oxford University Press Inc., New York, First published in the Oxford Classics series.
  8. Jiang, X. and Mahadevan, S. (2008), "Bayesian wavelet methodology for structural damage detection", Struct. Control Health Monit., 15, 974-991. https://doi.org/10.1002/stc.230
  9. Kass, R. and Raftery, A. (1995), "Bayes factors", J. Am. Stat. Assoc., 90(430), 773-795. https://doi.org/10.1080/01621459.1995.10476572
  10. Kim, C.W., Kawatani, M. and Kwon, Y.R. (2007), "Impact coefficient of reinforced concrete slabs on a steel girder bridge", Eng. Struct., 29(4), 576-590. https://doi.org/10.1016/j.engstruct.2006.05.021
  11. Kim, C.W., Sakakibara, T., Isemoto, R., Salpisoth, H., Oshima, Y. and Sugiura, K. (2011), "One year vibration monitoring of a short span bridge under in-service environments", Proceedings of the 5th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-5), Cancun, Mexico (CD-ROM).
  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. E., 8(5), 459-472. https://doi.org/10.1080/15732479.2010.539061
  13. Kim, C.W., Kitauchi, S., Sugiura, K. and Kawatani, M. (2013a), "A year-long monitoring using in-service vibration data from a multi-span plate-Gerber bridge", Proceedings of the Life-Cycle and Sustainability of Civil Infrastructure Systems, Strauss, (Eds., Frangopol and Bergmeister).
  14. Kim, C.W., Isemoto, R., Sugiura, K. and Kawatani, M. (2013b), "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
  15. Kim, C.W., Isemoto, R., Sugiura, K. and Kawatani, M. (2013c), "Linear system parameter as an indicator for structural diagnosis of short span bridges", Smart Struct. Syst., 11(1), 1-17. https://doi.org/10.12989/sss.2013.11.1.001
  16. Kitagawa, G. and Gersch, W. (1984), "A smoothness priors-state space modeling of time series with trend and seasonality", J. Am. Stat. Assoc., 79, 378-389.
  17. Moses, F. (1979), "Weigh-in-motion system using instrumented bridges", Transport. Eng. J., 105, TE3.
  18. Nair, K.K., Kiremidjian, A.S. and Law, K.H. (2006), "Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure", J. Sound Vib., 291(1-2), 349-368. https://doi.org/10.1016/j.jsv.2005.06.016
  19. Peeters, B. and De Roeck, G. (2001), "One-year monitoring of the Z24-Bridge: environmental effects versus damage events", Earthq. Eng. Struct. D., 30, 149-171. https://doi.org/10.1002/1096-9845(200102)30:2<149::AID-EQE1>3.0.CO;2-Z
  20. Sankararaman, S. and Mahadevan, S. (2011), "Bayesian methodology for diagnosis uncertainty quantification and health monitoring", Struct. Control Health Monit., 19, 88-106.
  21. Sohn, H., Worden, K. and Farrar, C.R. (2003), "Statistical damage classification under changing environmental and operational conditions", J. Intel. Mat. Systems Str., 13, 153-160.

Cited by

  1. Railway bridge structural health monitoring and fault detection: State-of-the-art methods and future challenges 2017, https://doi.org/10.1177/1475921717721137
  2. Automated, strain-based, output-only bridge damage detection pp.2190-5479, 2018, https://doi.org/10.1007/s13349-018-0311-6
  3. Long-term bridge health monitoring and performance assessment based on a Bayesian approach vol.14, pp.7, 2018, https://doi.org/10.1080/15732479.2018.1436572