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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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    1. 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.
    2. Xiao, Ning-Cong & Yuan, Kai & Zhan, Hongyou, 2022. "System reliability analysis based on dependent Kriging predictions and parallel learning strategy," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    3. 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.
    4. Jiang, Zhong-ming & Feng, De-Cheng & Zhou, Hao & Tao, Wei-Feng, 2021. "A recursive dimension-reduction method for high-dimensional reliability analysis with rare failure event," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    5. Zhou, Jin & Li, Jie, 2022. "An enhanced method for improving the accuracy of small failure probability of structures," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    6. Ameryan, Ala & Ghalehnovi, Mansour & Rashki, Mohsen, 2022. "AK-SESC: a novel reliability procedure based on the integration of active learning kriging and sequential space conversion method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    7. 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.
    8. Jiang, Chen & Qiu, Haobo & Yang, Zan & Chen, Liming & Gao, Liang & Li, Peigen, 2019. "A general failure-pursuing sampling framework for surrogate-based reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 47-59.
    9. Chen, Jiahui & Chen, Zhicheng & Xu, Yang & Li, Hui, 2021. "Efficient reliability analysis combining kriging and subset simulation with two-stage convergence criterion," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    10. Chen, Zequan & Li, Guofa & He, Jialong & Yang, Zhaojun & Wang, Jili, 2022. "Adaptive structural reliability analysis method based on confidence interval squeezing," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    11. Zhang, Jinhao & Xiao, Mi & Gao, Liang, 2019. "An active learning reliability method combining Kriging constructed with exploration and exploitation of failure region and subset simulation," Reliability Engineering and System Safety, Elsevier, vol. 188(C), pages 90-102.
    12. 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).
    13. Zhang, Yu & Dong, You & Xu, Jun, 2023. "An accelerated active learning Kriging model with the distance-based subdomain and a new stopping criterion for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    14. Wen, Zhixun & Pei, Haiqing & Liu, Hai & Yue, Zhufeng, 2016. "A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 153(C), pages 170-179.
    15. Chen, Zequan & Li, Guofa & He, Jialong & Yang, Zhaojun & Wang, Jili, 2022. "A new parallel adaptive structural reliability analysis method based on importance sampling and K-medoids clustering," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
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