An active learning reliability analysis algorithm: Based on the perspective of Kriging prediction variance

被引：0
|
作者：
Ouyang L. [1 ]
Huang L. [1 ]
Han M. [1 ]
机构：
[1] College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2023年 / 43卷 / 07期
基金：
中国国家自然科学基金;
关键词：
active learning function; Bootstrap; Kriging; Monte Carlo; reliability analysis;
D O I：
10.12011/SETP2022-2399
中图分类号：
学科分类号：
摘要：
Due to the underestimation of Kriging prediction variance, the active learning reliability method based on Kriging model suffers from errors in point selection and stopping judgments. An active learning reliability method based on Bootstrap Kriging (BAK-MCS) is proposed. First, the initial Kriging model of the actual limit state function is fitted, and then the BU learning function is constructed using Bootstrap Kriging variance to update the Kriging model by sequential sampling. Eventually, the convergent Kriging model combined with Monte Carlo simulation (MCS) is used to estimate the structural failure probability. Numerical examples show that compared to MCS and AK-MCS, BAK-MCS reduces the calls of the true limit state function while maintaining high prediction accuracy and improves the efficiency of reliability evaluation modeling. © 2023 Systems Engineering Society of China. All rights reserved.
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页码：2154 / 2165
页数：11
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