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A new adaptive analysis method based on the Kriging model for structural reliability analysis

Author

Listed:
  • Tianzhe Wang
  • Guofa Li
  • Haoming Zhu
  • Zhongshi Chen
  • Xiaoye Wang

Abstract

Widespread uncertainty in engineering problems makes it necessary to carry out structural reliability analysis. The crude Monte Carlo simulation (MCS) method can obtain accurate results, but it requires a large number of model evaluations. The Kriging-based method is a feasible way to reduce the computational cost. This study proposes a novel adaptive analysis method. Firstly, the convergence condition based on estimation accuracy is introduced. This condition focuses on the precision of the failure probability rather than the state of the points in the candidate sample pool. Then three extended U learning strategies are proposed. Sequence strategy (#1) focuses on evenly selecting samples by exploiting information on both sides of the limit state function. Strategy (#2) adopts the parallel adaptive learning technique to simultaneously select samples in both the safe and failure domains. Strategy (#3) pays attention to low-precision domains and can adaptively choose between sequential and parallel analysis modes. The choice of the three strategies can be based on the parallel computing resources available to researchers. Finally, three numerical cases and one engineering case are presented. This study provides an efficient tool for reliability evaluation of practical engineering problems.

Suggested Citation

  • Tianzhe Wang & Guofa Li & Haoming Zhu & Zhongshi Chen & Xiaoye Wang, 2025. "A new adaptive analysis method based on the Kriging model for structural reliability analysis," Journal of Risk and Reliability, , vol. 239(5), pages 1102-1114, October.
    • Handle: RePEc:sae:risrel:v:239:y:2025:i:5:p:1102-1114
    DOI: 10.1177/1748006X241296972
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    References listed on IDEAS

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    1. Cadini, F. & Santos, F. & Zio, E., 2014. "An improved adaptive kriging-based importance technique for sampling multiple failure regions of low probability," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 109-117.
    2. Echard, B. & Gayton, N. & Lemaire, M. & Relun, N., 2013. "A combined Importance Sampling and Kriging reliability method for small failure probabilities with time-demanding numerical models," Reliability Engineering and System Safety, Elsevier, vol. 111(C), pages 232-240.
    3. Dang, Chao & Wei, Pengfei & Faes, Matthias G.R. & Valdebenito, Marcos A. & Beer, Michael, 2022. "Parallel adaptive Bayesian quadrature for rare event estimation," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    4. Zhang, Xufang & Wang, Lei & Sørensen, John Dalsgaard, 2019. "REIF: A novel active-learning function toward adaptive Kriging surrogate models for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 440-454.
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