Structural Engineering and Mechanics

  • 제75권6호
  • /
  • Pages.771-784
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  • 2020
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A novel reliability analysis method based on Gaussian process classification for structures with discontinuous response

  • Zhang, Yibo (School of Mechanical Engineering and Automation, Northeastern University) ;
  • Sun, Zhili (School of Mechanical Engineering and Automation, Northeastern University) ;
  • Yan, Yutao (School of Mechanical Engineering and Automation, Northeastern University) ;
  • Yu, Zhenliang (School of Mechanical Engineering and Automation, Northeastern University) ;
  • Wang, Jian (School of Mechanical Engineering and Automation, Northeastern University)
  • 투고 : 2019.10.18
  • 심사 : 2020.05.01
  • 발행 : 2020.09.25
⟨ 이전 논문 다음 논문 ⟩

초록

Reliability analysis techniques combining with various surrogate models have attracted increasing attention because of their accuracy and great efficiency. However, they primarily focus on the structures with continuous response, while very rare researches on the reliability analysis for structures with discontinuous response are carried out. Furthermore, existing adaptive reliability analysis methods based on importance sampling (IS) still have some intractable defects when dealing with small failure probability, and there is no related research on reliability analysis for structures involving discontinuous response and small failure probability. Therefore, this paper proposes a novel reliability analysis method called AGPC-IS for such structures, which combines adaptive Gaussian process classification (GPC) and adaptive-kernel-density-estimation-based IS. In AGPC-IS, an efficient adaptive strategy for design of experiments (DoE), taking into consideration the classification uncertainty, the sampling uniformity and the regional classification accuracy improvement, is developed with the purpose of improving the accuracy of Gaussian process classifier. The adaptive kernel density estimation is introduced for constructing the quasi-optimal density function of IS. In addition, a novel and more precise stopping criterion is also developed from the perspective of the stability of failure probability estimation. The efficiency, superiority and practicability of AGPC-IS are verified by three examples.

키워드

과제정보

The financial supports of this research are from the National Natural Science Foundation of China (Grant NO. 51775097 and Grant NO. 51875095). The authors gratefully acknowledge their supports.

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