A dimension-wise analysis driven active learning paired-Kriging (DWA-ALK) method for the hybrid reliability analysis. (January 2023) - Shen, Chen; Ruan, Xiangdang; Liu, Tianxu; Xu, Menghui; Li, Ping
Abstract: Hybrid reliability analysis (HRA) has been extensively explored and mainly performed based on two categories of models. Mathematical properties on these models are given and proven in a rigorous manner, providing insights into HRA problems. Potential obstacles of the most appealing Kriging-based method available in literature for HRA include: metamodeling in an augmented-dimensional space spanned by both random and interval parameters resulting in substantial inversion of a large correlation matrix, an absence of distance metric in learning functions causing sample clustering, repeated interval analysis mainly completed by either the sampling-based or optimization-based algorithm leading to excessive computational cost and memory requirements especially for high-dimensional interval inputs. In this sense, a dimension-wise analysis driven active learning paired-Kriging method is proposed, where a paired-Kriging metamodel is constructed solely in the random input space and, at each training point, the interval analysis is conducted by a dimension-wise analysis method. A new learning function and termination criterion are conceived for refining the paired-Kriging model. It is concluded that the proposed method outperforms the common-in-use ones for HRA with high-dimensional interval inputs while its accuracy and efficiency are comparable in the case of low-dimensional interval inputs after validating its numerical performance by typical issues.