Structural Engineering and Mechanics

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A new structural reliability analysis method based on PC-Kriging and adaptive sampling region

  • Yu, Zhenliang (School of Mechanical and Power Engineering, Yingkou Institute of Technology) ;
  • Sun, Zhili (School of Mechanical Engineering and Automation, Northeastern University) ;
  • Guo, Fanyi (School of Mechanical Engineering and Automation, Northeastern University) ;
  • Cao, Runan (School of Mechanical Engineering and Automation, Northeastern University) ;
  • Wang, Jian (School of Mechanical Engineering and Automation, Northeastern University)
  • Received : 2020.08.08
  • Accepted : 2022.01.23
  • Published : 2022.05.10
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Abstract

The active learning surrogate model based on adaptive sampling strategy is increasingly popular in reliability analysis. However, most of the existing sampling strategies adopt the trial and error method to determine the size of the Monte Carlo (MC) candidate sample pool which satisfies the requirement of variation coefficient of failure probability. It will lead to a reduction in the calculation efficiency of reliability analysis. To avoid this defect, a new method for determining the optimal size of the MC candidate sample pool is proposed, and a new structural reliability analysis method combining polynomial chaos-based Kriging model (PC-Kriging) with adaptive sampling region is also proposed (PCK-ASR). Firstly, based on the lower limit of the confidence interval, a new method for estimating the optimal size of the MC candidate sample pool is proposed. Secondly, based on the upper limit of the confidence interval, an adaptive sampling region strategy similar to the radial centralized sampling method is developed. Then, the k-means++ clustering technique and the learning function LIF are used to complete the adaptive design of experiments (DoE). Finally, the effectiveness and accuracy of the PCK-ASR method are verified by three numerical examples and one practical engineering example.

Keywords

Acknowledgement

The research work financed with the means of the National Natural Science Foundation of China under Grant 51775097 and College Level Research Project Fund of Yingkou Institute of Technology 110504011.

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