新型结构可靠性分析自适应加点策略

doi:10.13229/j.cnki.jdxbgxb20200613

Abstract

Abstract:

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.

Key words: structural reliability, reliability analysis, surrogate model, adaptive sampling, learning function

CLC Number:

TB114.3

Guo-fa LI,Ze-quan CHEN,Jia-long HE. New adaptive sampling strategy for structural reliability analysis[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 1975-1981.

Figures/Tables 7

Fig.1

Fig.2

Table 1

Comparison of the surrogate models guidedby the method in this paper andMCS in numerical case"

分析方法	代理模型	样本量	失效概率	相对误差/%
MCS	-	$105$	0.014 63	-
本文方法	kriging模型	10+29	0.014 55	0.55
本文方法	RBF神经网络	10+56	0.014 69	0.41

Table 1

Fig.3

Table 2

Distribution of various variables"

变量	均值	标准差	分布类型
$m$	1	0.05	正态分布
$c 1$	1	0.1	正态分布
$c 2$	0.1	0.01	正态分布
$r$	0.05	0.05	正态分布
$F 1$	1	0.2	正态分布
$t 1$	1	0.2	正态分布

Table 2

Table 3

Comparison of the surrogate models guidedby the method in this paper andMCS in engineering case"

分析方法	代理模型	样本量	失效概率	相对误差/%
MCS	-	$105$	0.028 57	-
本文方法	kriging模型	10+27	0.028 39	0.63
本文方法	RBF神经网络	10+13	0.028 54	0.11

Table 3

Fig.4

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