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A new parallel adaptive structural reliability analysis method based on importance sampling and K-medoids clustering

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  • Chen, Zequan
  • Li, Guofa
  • He, Jialong
  • Yang, Zhaojun
  • Wang, Jili

Abstract

In using the Kriging-based adaptive structure reliability analysis methods, the key is to select the appropriate method of adding samples adaptively. In this study, a new parallel adaptive structure reliability analysis method—Reliability Analysis Based on Importance sampling and K-medoids clustering (RBIK)—is proposed. On the basis of the influence of the Kriging model's cognitive uncertainty on the estimation accuracy of failure probability, a global convergence condition (GCC) is proposed. Then, to evaluate the GCC unbiased and efficiently, the optimal importance sampling function will be constructed and used to obtain candidate samples. Considering the spatial correlation of candidate samples, the clustering algorithm is used for the cluster analysis of candidate samples to realize the parallel operation of adaptive structural reliability analysis. Therefore, RBIK is proposed on the basis of importance sampling and K-medoids clustering. RBIK strives to rapidly enable the Kriging model to satisfy the GCC rather than focusing on a single candidate sample, which is the most obvious difference between RBIK and other adaptive structural reliability analysis methods. In addition, RBIK can balance parallel computing power, accuracy, and the number of iterations required. Finally, the effectiveness and robustness of RBIK are proven by several examples.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:reensy:v:218:y:2022:i:pa:s0951832021006190
    DOI: 10.1016/j.ress.2021.108124
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    References listed on IDEAS

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    1. Sun, Zhili & Wang, Jian & Li, Rui & Tong, Cao, 2017. "LIF: A new Kriging based learning function and its application to structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 152-165.
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    3. Teixeira, Rui & Nogal, Maria & O’Connor, Alan & Martinez-Pastor, Beatriz, 2020. "Reliability assessment with density scanned adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
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    Cited by:

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    4. Ma, Yuan-Zhuo & Zhu, Yi-Chen & Li, Hong-Shuang & Nan, Hang & Zhao, Zhen-Zhou & Jin, Xiang-Xiang, 2022. "Adaptive Kriging-based failure probability estimation for multiple responses," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
    5. 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).
    6. 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).
    7. Wang, Tianzhe & Chen, Zequan & Li, Guofa & He, Jialong & Liu, Chao & Du, Xuejiao, 2024. "A novel method for high-dimensional reliability analysis based on activity score and adaptive Kriging," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    8. 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).

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