A Sequential Kriging reliability analysis method with characteristics of adaptive sampling regions and parallelizability

被引：151

作者：

Wen, Zhixun ^{[1
]}

Pei, Haiqing ^{[1
]}

Liu, Hai ^{[1
]}

Yue, Zhufeng ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Dept Engn Mech, Xian 710072, Peoples R China

来源：

RELIABILITY ENGINEERING & SYSTEM SAFETY | 2016年 / 153卷

基金：

中国国家自然科学基金;

关键词：

Reliability; Kriging; Surrogate model; Parallelizability; SENSITIVITY-ANALYSIS; DESIGN; SIMULATION;

D O I：

10.1016/j.ress.2016.05.002

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The sequential Kriging reliability analysis (SKRA) method has been developed in recent years for non-linear implicit response functions which are expensive to evaluate. This type of method includes EGRA: the efficient reliability analysis method, and AK-MCS: the active learning reliability method combining Kriging model and Monte Carlo simulation. The purpose of this paper is to improve SKRA by adaptive sampling regions and parallelizability. The adaptive sampling regions strategy is proposed to avoid selecting samples in regions where the probability density is so low that the accuracy of these regions has negligible effects on the results. The size of the sampling regions is adapted according to the failure probability calculated by last iteration. Two parallel strategies are introduced and compared, aimed at selecting multiple sample points at a time. The improvement is verified through several troublesome examples. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：170 / 179

页数：10

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