湖南大学学报（自然科学版）2025，Vol.52Issue(6)：120-133,14.DOI:10.16339/j.cnki.hdxbzkb.2025182
一种基于证据理论的主动学习可靠性分析方法
An Active Learning Reliability Analysis Method Based on Evidence Theory
摘要
Abstract
For the reliability analysis problem characterized by a single failure mode,cognitive uncertainty,and"black-box"models,an active learning reliability analysis method based on evidence theory is proposed.This method efficiently and accurately determines the credibility and verisimilitude of structures.It handles cognitive uncertain variables using evidence theory,initiates initial training sample construction for a Kriging model,and combines optimization methods with active learning to search for optimal training samples across the entire input variable space.This approach refines the Kriging model chronically with optimal training samples,replacing the functional function with the Kriging model to predict unknown points for credibility and verisimilitude calculation of the structure.By integrating optimization methods with active learning,the method relaxes constraints on candidate sample locations during traditional training sample search,thereby identifying training samples that better enhance the Kriging model's correction effects and improve the efficiency and success rate of Kriging model construction.Numerical examples demonstrate the method's computational effectiveness and its application to the reliability analysis of vehicle frontal collisions.关键词
结构可靠性/可靠性分析/证据理论/"黑箱"问题/主动学习Kriging模型Key words
structural reliability/reliability analysis/evidence theory/black box problem/active learning kriging model分类
通用工业技术引用本文复制引用
张哲,宝文礼,姚中洋..一种基于证据理论的主动学习可靠性分析方法[J].湖南大学学报（自然科学版）,2025,52(6):120-133,14.基金项目
国家自然科学基金资助项目(52375242),National Natural Science Foundation of China(52375242) （52375242）
湖南省自然科学基金资助项目(2023JJ20011),Natural Science Foundation of Hunan Province(2023JJ20011) （2023JJ20011）
