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Stochastic collocation enhanced line sampling method for reliability analysis

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  • Wei, Ning
  • Lu, Zhenzhou
  • Hu, Yingshi

Abstract

The stochastic collocation enhanced line sampling (SC-LS) method for reliability analysis is proposed in this paper, which is the point estimation strategy within the framework of line sampling. Instead of utilizing the direct random sampling strategy to estimate the integration of the one-dimensional conditional failure probability function in the traditional line sampling approach, the proposed SC-LS method evaluate the integration directly by Gaussian quadrature combined with the univariate multiplicative dimensionality reduction method (U-MDRM) or bivariate multiplicative dimensionality reduction method (B-MDRM), and the failure probability is estimated as the weighted sum of the one-dimensional conditional failure probabilities at the generated Gaussian quadrature grids. The superiority of proposed SC-LS method is demonstrated by several numerical examples and finite element examples, and the proposed method has two advantages, one is that it avoids the random sampling in one-dimension-reduced space, which is required by traditional line sampling approach, hence no variability exists in the estimate of failure probability. The other one is that it can be used for the small failure probability problems and is much less sensitive to the choice of importance direction.

Suggested Citation

  • Wei, Ning & Lu, Zhenzhou & Hu, Yingshi, 2023. "Stochastic collocation enhanced line sampling method for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:reensy:v:240:y:2023:i:c:s0951832023004660
    DOI: 10.1016/j.ress.2023.109552
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    References listed on IDEAS

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    1. Wang, Cao & Zhang, Hao & Li, Quanwang, 2019. "Moment-based evaluation of structural reliability," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 38-45.
    2. Zhang, Xiaobo & Lu, Zhenzhou & Cheng, Kai, 2022. "Cross-entropy-based directional importance sampling with von Mises-Fisher mixture model for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
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    Cited by:

    1. Hussain, Muhammad & Zhang, Tieling, 2025. "Machine learning-based outlier detection for pipeline in-line inspection data," Reliability Engineering and System Safety, Elsevier, vol. 254(PA).

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