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An enhanced method for improving the accuracy of small failure probability of structures

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  • Zhou, Jin
  • Li, Jie

Abstract

This paper presents a hierarchical partitioning strategy (HP) for probability space to improve the accuracy of metamodel and avoid the memory problems of computer in dealing with small failure probability events. By the continuous partition of probability space, the construction of adaptive Kriging metamodel is intelligently divided into two steps. Thereafter, the well-trained Kriging via two steps is used to approximate the relationship between the extreme value of response and basic random variables of the system. By combining the probability density evolution method with well-trained Kriging metamodel, the reliability of the investigated stochastic system can be readily obtained with a one-dimensional integration operation. Subsequently, a new reliability analysis method is proposed, called HP-AK-PDEM (Hierarchical Partitioning strategy-based Adaptive Kriging combining Probability Density Evolution Method). To demonstrate the accuracy and efficiency of the proposed method, three analytical performance functions with nonlinear features and a ten-story shear-frame structure are addressed. The comparative study of different reliability methods is carried out as well. Numerical results demonstrate that the presented method has better accuracy and higher efficiency in dealing with the problems of small and rare failure probability issues encountered in engineering structures.

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  • 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).
    • Handle: RePEc:eee:reensy:v:228:y:2022:i:c:s0951832022004033
    DOI: 10.1016/j.ress.2022.108784
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    References listed on IDEAS

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    7. Wang, Jinsheng & Xu, Guoji & Li, Yongle & Kareem, Ahsan, 2022. "AKSE: A novel adaptive Kriging method combining sampling region scheme and error-based stopping criterion for structural reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
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    Cited by:

    1. Li, Jin-Yang & Lu, Jubin & Zhou, Hao, 2023. "Reliability analysis of structures with inerter-based isolation layer under stochastic seismic excitations," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
    2. Wu, Yongxin & Wang, Juncheng & Cheng, Jialiang & Yang, Shangchuan, 2024. "Dimension-Reduction Spectral Representation of Soil Spatial Variability and Its Application in the Efficient Reliability Analysis of Seismic Response in Tunnels," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    3. Millar, Robert & Li, Hui & Li, Jinglai, 2023. "Multicanonical sequential Monte Carlo sampler for uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    4. 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).
    5. Xu, Zidong & Wang, Hao & Zhao, Kaiyong & Zhang, Han & Liu, Yun & Lin, Yuxuan, 2024. "Evolutionary probability density reconstruction of stochastic dynamic responses based on physics-aided deep learning," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    6. Li, Guosheng & Ma, Shuaichao & Zhang, Dequan & Yang, Leping & Zhang, Weihua & Wu, Zeping, 2024. "An efficient sequential anisotropic RBF reliability analysis method with fast cross-validation and parallelizability," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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