新型结构可靠性分析自适应加点策略  

New adaptive sampling strategy for structural reliability analysis

作　　者：李国发[1,2] 陈泽权 何佳龙 LI Guo-fa;CHEN Ze-quan;HE Jia-long(Key Laboratory of CNC Equipment Reliability,Ministry of Education,Jilin University,Changchun 130022,China;College of Mechanical and Aerospace Engineering,Jilin University,Changchun 130022,China)

机构地区：[1]吉林大学数控装备可靠性教育部重点实验室,长春130022 [2]吉林大学机械与航空航天工程学院,长春130022

出　　处：《吉林大学学报（工学版）》2021年第6期1975-1981,共7页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(51905209);吉林省中青年科技创新领军人才及团队项目(20190101015JH);吉林大学高层次科技创新团队项目(JLUSTIRT)。

摘　　要：为了在进行结构可靠性分析时,能够构建高精度、高效率的代理模型,提出了一种面向多种代理模型的基于通用学习函数(GLF)的结构可靠性分析自适应加点策略。该策略被视为一个多目标优化过程,GLF考虑了样本点间的平均距离和最小距离、是否分布在极限状态函数的附近以及联合概率密度函数等因素,使得自适应添加的样本点能稳健、高效地提升代理模型对失效概率的估计精度。数值案例和工程案例结果表明,针对不同的代理模型,GLF能够利用少量的样本点,高精度、高效率地估计出结构的失效概率。In structural reliability analysis, choosing an appropriate adaptive sampling strategy is the key to constructing a high-precision and high-efficiency surrogate model. An adaptive sampling strategy for structural reliability analysis based on General Learning Function(GLF) for multiple surrogate models is proposed. The adaptive sampling strategy is regarded as a multi-objective optimization process, so the average and the minimum distance between the sample points, whether they are distributed near the limit state function, and the probability density function are all considered by the GLF to ensure that the new sample points can robustly and efficiently improve the surrogate model estimation accuracy of failure probability. Numerical cases and engineering case show that for different surrogate models, the GLF can use a small number of sample points to estimate the structural failure probability with high accuracy and efficiency.

关 键 词：结构可靠性 可靠性分析 代理模型 自适应加点 学习函数 

分 类 号：TB114.3[理学—概率论与数理统计]