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A novel kriging based active learning method for structural reliability analysis

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Abstract

In the reliability analysis of engineering structures, there are usually implict and highly nonlinear performance function problems, which leads to the time-consuming computations. In this paper, a novel Kriging based reliability analysis method combined with the improved efficient global optimization (IEGO) and a secondary point selection strategy is proposed. Based on the IEGO algorithm, the expected improvement function is redefining, which will focus on the points both with large variance and near the limit state surface. Moreover, a secondary point selection strategy is raised to find the point with larger expected improvement and closed to the limit state surface, which can further improve the efficiency of the active learning process. Five examples indicates that the raised method has satisfactory global and local search capability, and can evaluate the probability of failure efficiently.

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Acknowledgments

The financial supports from the National Science and Technology Major Project of China (Grant No. 2017-V-0013-0065 and Grant No.2017-V-0010-0060) are gratefully acknowledged.

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Authors and Affiliations

  1. School of Power and Energy, Northwestern Polytechnical University, Xi’an, 710072, China

    Hong Linxiong, Li Huacong, Peng Kai & Xiao Hongliang

Authors
  1. Hong Linxiong
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  2. Li Huacong
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  3. Peng Kai
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  4. Xiao Hongliang
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Corresponding author

Correspondence to Hong Linxiong.

Additional information

Recommended by Editor Chongdu Cho

Hong Linxiong is a Ph.D. candidate in the School of Power and Energy, Northwestern Polytechnical University. His research interests include structural reliability, aero-engine fuel system and others.

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Linxiong, H., Huacong, L., Kai, P. et al. A novel kriging based active learning method for structural reliability analysis. J Mech Sci Technol 34, 1545–1556 (2020). https://doi.org/10.1007/s12206-020-0317-y

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  • DOI: https://doi.org/10.1007/s12206-020-0317-y

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