.png){kind=icon}
返回
An active-learning method based on multi-fidelity Kriging model for structural reliability analysis
.png){kind=icon}
PRE
AI
DOI：10.1007/s00158-020-02678-1.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Active-learning surrogate model-based reliability analysis is widely employed in engineering structural reliability analysis to alleviate the computational burden of the Monte Carlo method. To date, most of these methods are built based on the single-fidelity surrogate model, such as the Kriging model. However, the computational burden of constructing a fine Kriging model may be still expensive if the high-fidelity (HF) simulation is extremely time-consuming. To solve this problem, an active-learning method based on the multi-fidelity (MF) Kriging model for structural reliability analysis (abbreviated as AMK-MCS+AEFF), which is an online data-driven method fusing information from different fidelities, is proposed in this paper. First, an augmented expected feasibility function (AEFF) is defined by considering the cross-correlation, the sampling density, and the cost query between HF and low-fidelity (LF) models. During the active-learning process of AMK-MCS+AEFF, both the location and fidelity level of the updated sample can be determined objectively and adaptively by maximizing the AEFF. Second, a new stopping criterion that associates with the estimated relative error is proposed to ensure that the iterative process terminates in a proper iteration. The proposed method is compared with several state-of-the-art methods through three numerical examples and an engineering case. Results show that the proposed method can provide an accurate failure probability estimation with a less computational cost.
Keyword:
Structural reliability analysis
Expected feasibility function
Failure probability
Multi-fidelity Kriging model
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4
论文数:
4.8K
被引数:
1.7W
机构
引用论文
Application of line sampling simulation method to reliability benchmark problems线抽样模拟法在可靠性基准问题中的应用.png)
STRUCTURAL SAFETY
STRUCTURAL SAFETYIF6.3
PREAI
分享收藏A generalized hierarchical co-Kriging model for multi-fidelity data fusion用于多保真度数据融合的广义分层协同Kriging模型
PREAI
分享收藏Improved response surface method and its application to stiffened plate reliability analysis改进的响应面法及其在加筋板可靠性分析中的应用
PREAI
分享收藏
查看更多内容