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A Kriging-assisted adaptive improved cross-entropy importance sampling method for random-interval hybrid reliability analysis

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Abstract

This paper proposes an efficient Kriging-assisted improved cross-entropy (ICE) method for random-interval hybrid reliability analysis. This method employs Kriging to substitute for the projection outlines of limit state surface and incrementally updates the Kriging model within the final layer samples of ICE. A novel learning function that combines both the lower and upper confidence bounds for extremum searching is proposed to identify samples on the projection outlines. Additionally, an error-based stopping criterion (EBSC) is proposed to avoid unnecessary updates to the Kriging model. The efficiency and effectiveness of the proposed method are demonstrated through four benchmark examples. Furthermore, the method is applied to two practical engineering scenarios: the strength reliability analysis of a bogie and the resonance reliability analysis of an axially functionally graded material (FGM) pipeline. The results indicate that the proposed method achieves high levels of efficiency and accuracy.

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Acknowledgements

This work was supported by National Natural Science Foundation of China [Grant No. 52475168] and Fundamental Research Funds for the Central Universities (Grant No. 2682022ZTPY079).

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Authors and Affiliations

  1. School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi’an, China

    Xin Fan & Yongshou Liu

  2. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China

    Xufeng Yang

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  1. Xin Fan
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Correspondence to Xufeng Yang.

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Fan, X., Yang, X. & Liu, Y. A Kriging-assisted adaptive improved cross-entropy importance sampling method for random-interval hybrid reliability analysis. Struct Multidisc Optim 67, 158 (2024). https://doi.org/10.1007/s00158-024-03865-0

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