Quantile-based optimization under uncertainties for complex engineering structures using an active learning basis-adaptive PC-Kriging model  

作　　者：Yulian GONG Jianguo ZHANG Dan XU Ying HUANG 

机构地区：[1]School of Reliability and Systems Engineering,Beihang University,Beijing 100191,China

出　　处：《Chinese Journal of Aeronautics》2025年第1期340-352,共13页中国航空学报(英文版)

基　　金：supported by the National Key R&D Program of China(No.2021YFB1715000);the National Natural Science Foundation of China(No.52375073)。

摘　　要：The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.

关 键 词：Reliability-based design optimization Quantile-based Basis-adaptive PC-Kriging Complex engineering structures Active learning Uncertainty 

分 类 号：TB114.3[理学—概率论与数理统计] TP18[理学—数学] V22[理学—应用数学]