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一种基于自适应Kriging集成模型的结构可靠性分析方法OA北大核心CSTPCD

A Structural Reliability Calculation Method Based on Adaptive Kriging Ensemble Model

中文摘要英文摘要

基于Kriging模型的复杂结构可靠性分析结果高度依赖于Kriging模型的拟合精度,在构建Kriging模型的过程中,不同相关函数和回归函数的选择均会影响模型精度.为解决模型的不确定性对可靠性分析结果的影响,同时兼顾计算效率和精度,基于Kriging模型和蒙特卡罗模拟(MCS)方法,提出了一种结合自适应集成策略和主动学习函数的结构可靠度计算方法.该方法考虑Kriging模型的建模不确定性,将多种Kriging模型组合,构建了一种综合考虑样本点贡献和样本点距离的主动学习函数,通过主动学习函数迭代更新集成Kriging模型直至满足收敛条件,最后通过构建的集成Kriging模型和MCS方法进行结构可靠性分析.数值算例和工程算例结果验证了所提方法的有效性,该方法与其他主要方法相比稳健性更好,在保证计算精度的同时,计算效率更高.

The reliability analysis results of complex structures based on the Kriging model were highly dependent on the fitting accuracy of the Kriging model.In the constructing processes of the Kriging model,the selection of different correlation and regression functions affected the accuracy of the model.In order to solve the impacts of model uncertainty on the reliability analysis results,while considering computational efficiency and accuracy,based on the Kriging model and Monte Carlo simu-lation(MCS)method,a structural reliability calculation method combining adaptive ensemble strategy and active learning function was proposed.Considering the modeling uncertainty of Kriging models,combined with multiple Kriging models,this methed constructed an active learning function that com-prehensively considered sample point contribution and sample point distance.The ensemble Kriging model was iteratively updated through the active learning function until the convergence conditions were satisfied.Finally the structural reliability analysis was carried out by the constructed ensemble Kriging model and MCS method.The validity of the proposed method was verified by numerical and engineering examples,and the results show that the proposed method is more robust than other major methods,and the computational efficiency is higher while ensuring the computational accuracy.

高进;崔海冰;樊涛;李昂;杜尊峰

潍柴动力股份有限公司,潍坊,261061||内燃机可靠性国家重点实验室,潍坊,261061天津大学建筑工程学院,天津,300354

机械工程

结构可靠性自适应集成策略Kriging模型主动学习函数

structural reliabilityadaptive ensemble strategyKriging modelactive learning function

《中国机械工程》 2024 (001)

83-92 / 10

内燃机可靠性国家重点实验室开放课题(skler-202112)

10.3969/j.issn.1004-132X.2024.01.008

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