New adaptive sampling strategy for structural reliability analysis

被引：0

作者：

Li G.-F. ^{[1
,2
]}

Chen Z.-Q. ^{[1
,2
]}

He J.-L. ^{[1
,2
]}

机构：

[1] Key Laboratory of CNC Equipment Reliability, Ministry of Education, Jilin University, Changchun

[2] College of Mechanical and Aerospace Engineering, Jilin University, Changchun

来源：

He, Jia-Long (hejl@jlu.edu.cn); He, Jia-Long (hejl@jlu.edu.cn) | 1975年 / Editorial Board of Jilin University卷 / 51期

关键词：

Adaptive sampling; Learning function; Reliability analysis; Structural reliability; Surrogate model;

D O I：

10.13229/j.cnki.jdxbgxb20200613

中图分类号：

学科分类号：

摘要：

In structural reliability analysis, choosing an appropriate adaptive sampling strategy is the key to constructing a high-precision and high-efficiency surrogate model. An adaptive sampling strategy for structural reliability analysis based on General Learning Function (GLF) for multiple surrogate models is proposed. The adaptive sampling strategy is regarded as a multi-objective optimization process, so the average and the minimum distance between the sample points, whether they are distributed near the limit state function, and the probability density function are all considered by the GLF to ensure that the new sample points can robustly and efficiently improve the surrogate model estimation accuracy of failure probability. Numerical cases and engineering case show that for different surrogate models, the GLF can use a small number of sample points to estimate the structural failure probability with high accuracy and efficiency. © 2021, Jilin University Press. All right reserved.

引用

页码：1975 / 1981

页数：6

共 20 条

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