华中科技大学学报（自然科学版）2024，Vol.52Issue(1)：99-105,7.DOI:10.13245/j.hust.240298
基于新学习函数与收敛准则的改进AK-MCS方法
Improved AK-MCS method based on new learning function and convergence criterion
摘要
Abstract
On the basis of error analysis of the AK-MCS method and considering the impact of Kriging model updates on errors,a new learning function and convergence criterion were proposed.Firstly,according to statistical properties of Kriging model,the error analysis of the AK-MCS method for predicting failure probability was performed.Secondly,due to influence of adding training sample on the error contribution,a learning function considering regional influence was proposed,and then a two-step method for selecting training sample was presented,in which the differences of both deterministic error and statistical one for different training samples were involved.Thirdly,considering accuracy of reliability and stability of model convergence,a convergence criterion was proposed.Combining the new strategy for selecting training sample and the new convergence criterion,an improved AK-MCS for reliability analysis was presented.Finally,several examples were employed to verify the applicability of the proposed method for problems concerning high nonlinearity,multiple failure domains higher dimensionality and finite element engineering problems,what is more,the results showed that the method is of high accuracy,efficiency and stability.关键词
结构可靠度/Kriging模型/主动学习/收敛准则/误差估计Key words
structural reliability/Kriging model/active learning/convergence criterion/error estimation分类
通用工业技术引用本文复制引用
范文亮,余书君,李正良..基于新学习函数与收敛准则的改进AK-MCS方法[J].华中科技大学学报（自然科学版）,2024,52(1):99-105,7.基金项目
国家自然科学基金资助项目(52178455,51678092) （52178455,51678092）
中能建规划设计集团科技资助项目(GSKJ2-T05-2020). （GSKJ2-T05-2020）
