期刊
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
卷 51, 期 5, 页码 1003-1016出版社
SPRINGER
DOI: 10.1007/s00158-014-1189-5
关键词
Hybrid reliability; Random variable; Interval variable; Active learning; Kriging model
资金
- Aerospace Support Fund [NBXW0001]
- Foundation Research Funds of Northwestern Polytechnical University [JCY20130123]
Hybrid reliability analysis (HRA) with both random and interval variables is investigated in this paper. Firstly, it is figured out that a surrogate model just rightly predicting the sign of performance function can meet the requirement of HRA in accuracy. According to this idea, a methodology based on active learning Kriging (ALK) model named ALK-HRA is proposed. When constructing the Kriging model, the presented method only finely approximates the performance function in the region of interest: the region where the sign tends to be wrongly predicted. Based on the constructed Kriging model, Monte Carlo Simulation (MCS) is carried out to estimate both the lower and upper bounds of failure probability. ALK-HRA is accurate enough with calling the performance function as few times as possible. Four numerical examples and one engineering application are investigated to demonstrate the performance of the proposed method.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据