Smart Structures and Systems

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Particle filter for model updating and reliability estimation of existing structures

  • Received : 2012.06.11
  • Accepted : 2012.11.30
  • Published : 2013.01.25
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Abstract

It is essential to update the model with reflecting observation or inspection data for reliability estimation of existing structures. Authors proposed updated reliability analysis by using Particle Filter. We discuss how to apply the proposed method through numerical examples on reinforced concrete structures after verification of the method with hypothetical linear Gaussian problem. Reinforced concrete structures in a marine environment deteriorate with time due to chloride-induced corrosion of reinforcing bars. In the case of existing structures, it is essential to monitor the current condition such as chloride-induced corrosion and to reflect it to rational maintenance with consideration of the uncertainty. In this context, updated reliability estimation of a structure provides useful information for the rational decision. Accuracy estimation is also one of the important issues when Monte Carlo approach such as Particle Filter is adopted. Especially Particle Filter approach has a problem known as degeneracy. Effective sample size is introduced to predict the covariance of variance of limit state exceeding probabilities calculated by Particle Filter. Its validity is shown by the numerical experiments.

Keywords

References

  1. Akiyama, M., Frangopol, D.M. and Yoshida, I. (2010), "Time-dependent reliability analysis of existing RC structures in a marine environment using hazard associated with airborne chlorides", Eng. Struct., 32(11), 3768-3779. https://doi.org/10.1016/j.engstruct.2010.08.021
  2. Alspach, D.L. and Sorensen, H.W. (1972), "Nonlinear Bayesian estimation using Gaussian sum approximations", IEEE T. Autom. Contr., 17(4), 439-448. https://doi.org/10.1109/TAC.1972.1100034
  3. Arulampalam, S., Maskell, S., Gordon N. and Clapp, T. (2002), "A tutorial on particle filters for on-line non-linear/non-Gaussian Bayesian tracking", IEEE T. Signal Process., 50(20), 174-188. https://doi.org/10.1109/78.978374
  4. Au, S.K. (2011), "Fast Bayesian FFT method for ambient modal identification with separated modes", J. Eng. Mech. ASCE, 137(3), 214-226. https://doi.org/10.1061/(ASCE)EM.1943-7889.0000213
  5. Ellingwood, B.R. (2005), "Risk-informed condition assessment of civil infrastructure: state of practice and research issues", Struct. Infrastruct. E., 1(1), 7-18. https://doi.org/10.1080/15732470412331289341
  6. Frangopol, D.M. (2009), Life-cycle performance, management, and optimization of structural systems under uncertainty: accomplishments and challenges (Keynote paper), ICOSSAR, 38-60, Osaka, Japan.
  7. Evensen, G. (1994), "Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics", J. Geophys. Res., 99, 10143-10162. https://doi.org/10.1029/94JC00572
  8. Ge, L. and Soong T.T. (1998), "Damage identification through regularization method. I: theory", J. Eng. Mech.- ASCE, 124(1), 103-108. https://doi.org/10.1061/(ASCE)0733-9399(1998)124:1(103)
  9. Gordon, N., Salmond, D. and Smith, A. (1993), "A novel approach to nonlinear / non-Gaussian Bayesian state estimation", Proceedings of the IEEE on Radar and Signal Processing.
  10. Hoshiya, M. and Saito, E. (1984), "Structural identification by extended Kalman Filter", J. Eng. Mech.-ASCE, 110(12), 1757-1770. https://doi.org/10.1061/(ASCE)0733-9399(1984)110:12(1757)
  11. Honjo, Y., Wen-Tsung, L. and Sakajo, S. (1994), "Application of akaike information criterion statistics to geotechnical inverse analysis: the extended Bayesian method", Struct. Saf., 14(1-2), 5-29. https://doi.org/10.1016/0167-4730(94)90004-3
  12. Hoshiya, M. and Yoshida, I. (1996), "Identification of conditional stochastic Gaussian field", J. EM.-ASCE, 122(2), 101-108.
  13. Hoshiya, M. and Yoshida, I. (1998), "Process noise and optimum observation in conditional stochastic fields", J. EM.-ASCE, 124(12), 1325-1330 .
  14. Kitagawa, G. (1996), "Monte Carlo Filter and smoother for Non-Gaussian state space models", J. Comput. Graph. Stat., 5(1), 1-25.
  15. Julier, S.J. and Uhlmann, J.K. (1997), "A New Extension of the Kalman Filter to nonlinear Systems, Proceedings of AeroSense", Proceedings of the 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, Multi Sensor Fusion, Tracking and Resource Management II, SPIE.
  16. Nakano, S., Ueno, G. and Higuchi, H. (2007), "Merging particle filter for sequential data assimilation, Nonlinear Proc. Geoph., 14(4), 395-408 https://doi.org/10.5194/npg-14-395-2007
  17. Ristic, B., Arulampalam, S. and Gordon, N. (2004), Beyond the Kalman Filter, Particle Filters for Tracking Applications, Artech House.
  18. Sato, T. and Kaji, K. (2000), "Adaptive Monte Carlo Filter and structure identification", Proceedings of the first International Conference on Monte Carlo Simulation, Monte Carlo, Monaco
  19. Yoshida, I. and Sato, T. (2002), "Health monitoring algorithm by the Monte Carlo Filter based on non-Gaussian noise", J. Nat. Disaster Sci., 24(2), 101-107.
  20. Yoshida, I., Akiyama, M. and Suzuki, S. (2009), "Reliability analysis of an existing RC structure updated by inspection data", Proceedings of the 10th International Conference on Structural Safety and Reliability.
  21. Yun, C.B. and Shinozuka, M. (1980), "Identification pf nonlinear structural dynamic systems", J. Struct. Mech.-ASCE, 8(2), 187-203. https://doi.org/10.1080/03601218008907359

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