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RBIK-SS: A parallel adaptive structural reliability analysis method for rare failure events

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  • Li, Guofa
  • Wang, Tianzhe
  • Chen, Zequan
  • He, Jialong
  • Wang, Xiaoye
  • Du, Xuejiao

Abstract

One of the important challenges in structural reliability is using only a few function calls to obtain an accurate failure probability. To solve this problem, a combination of the adaptive Kriging method and Monte Carlo simulation (i.e., AK-MCS) has been proposed. However, AK-MCS may not be the most efficient method to estimate small failure probabilities because of the large candidate sample pool. This study proposes a new method that combines the reliability analysis method based on importance sampling and k-medoids clustering (RBIK) with subset simulation (SS) to estimate small failure probabilities. The proposed method replaces the MCS sample pool in RBIK with a smaller SS population to overcome the limitations of computer memory. Considering the influence of epistemic uncertainty on SS conditional samples, the conditional quasi-optimal importance distribution is derived. Then, a partial convergence condition (PCC) is introduced to control the estimation accuracy of failure probability. To accelerate the convergence of PCC, the parallel addition strategy based on the partial optimal importance sampling function and k-medoids method is presented. Finally, four examples (i.e., two numerical and two engineering examples) are studied. The results illustrate that RBIK-SS can solve rare failure events with satisfactory accuracy and efficiency.

Suggested Citation

  • Li, Guofa & Wang, Tianzhe & Chen, Zequan & He, Jialong & Wang, Xiaoye & Du, Xuejiao, 2023. "RBIK-SS: A parallel adaptive structural reliability analysis method for rare failure events," Reliability Engineering and System Safety, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:reensy:v:239:y:2023:i:c:s0951832023004271
    DOI: 10.1016/j.ress.2023.109513
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    References listed on IDEAS

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