Surrogate modeling immersed probability density evolution method for structural reliability analysis in high dimensions

In conjunction with advanced surrogate modeling methods, an improved scheme of probability density evolution method (PDEM) is presented to tackle with the challenge inherent in high-dimensional structural reliability analysis. In this method, the KPCA-GPR model is developed, where the kernel principal component analysis (KPCA)-based nonlinear dimension reduction and the Gaussian process regression (GPR) surrogate model are combined via a joint-training scheme. In this regard, the identified KPCA-based subspace is optimal to the approximation accuracy of the resultant GPR model. Then, the KPCA-GPR model is constructed using the active learning (AL)-based sampling strategy, so as to accurately approximate the equivalent extreme-value (EEV) of structural responses at the whole representative point set involved in the PDEM with as fewer samples as possible. Finally, the reliability is readily evaluated by the one-dimensional integral of the EEVs' probability density function derived from the PDEM. To illustrate the effectiveness of the proposed ALKPCA-GPR-PDEM, two numerical examples are studied, involving the reliability analysis of both nonlinear analytical functions with different dimensions and shear-frame structures under earthquake ground motions. Numerical results indicate that massive computational cost savings and desirable accuracy enhancement are achieved by the AL-KPCAGPR-PDEM when dealing with the reliability problems in high dimensions. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

An improved scheme of probability density evolution method (PDEM) is presented to tackle high-dimensional structural reliability analysis challenges, using the KPCA-GPR model and an active learning-based sampling strategy, achieving significant computational cost savings and accuracy enhancement in numerical examples.