一种面向多保真Kriging模型结构可靠性分析的主动学习方法

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关键词：结构可靠性；主动学习；多保真Kriging模型；保真度选择策略中图分类号：TP181;TB114.3DOI：10.3969/j.issn.1004-132X.2026.02.018 开放科学（资源服务）标识码（OSID）：

A New Active Learning Method for Structural Reliability Analysis of Multifidelity Kriging Models

DU Zunfengl* FAN Tao² JIANG Dengyao1 1.State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation，Tianjin University，Tianjin，300354 2.AVIC the First Aircraft Institute，Xi'an，710089

Abstract： A structural reliability method was proposed based on multi-fidelity Kriging modeling with active learning，which determined the computational and spatial locations of sample points during each iteration through a three-stage selection. Firstly，the optimal set of sample points was determined by ensemble multiple learning functions. Secondly，the computational locations of the sample points were determined by the proposed BES（beneficial effect strategy）.Finally，the spatial locations of the sample points were determined from the optimal set of sample points by applying Bootstrap sampling method. The effectiveness and efficiency of the method was demonstrated by two numerical examples and one practical engineering example.Compared with the curent advanced multi-fidelity model structure reliability method，when the fidelity of the model is lower，the computational failure maybeeffctively avoided，which shows the advanced and better applicability of the method.

Key words： structural reliability；active learning；multi-fidelity Kriging model； fidelity selection strat egy

0 引言

随着复杂机械结构与工程结构所受的环境载荷愈发恶劣，不确定性因素对结构可靠性的影响也愈发重要]，针对多源随机不确定性下的复杂结构可靠性评估，蒙特卡罗模拟（MonteCarlosimulation，MCS）通过直接随机生成大量模拟样本以求得稳健的可靠性结果而被认为是准确的结果，但当复杂结构的响应计算涉及大量的有限元计算时，MCS的计算成本极高，往往不具备可行性。（剩余21583字）