Structural reliability analysis based on ensemble learning of surrogate models

Assessing the failure probability of complex structure is a difficult task in presence of various uncertainties. In this paper, a new adaptive approach is developed for reliability analysis by ensemble learning of multiple competitive surrogate models, including Kriging, polynomial chaos expansion and support vector regression. Ensemble of surrogates provides a more robust approximation of true performance function through a weighted average strategy, and it helps to identify regions with possible high prediction error. Starting from an initial experimental design, the ensemble model is iteratively updated by adding new sample points to regions with large prediction error as well as near the limit state through an active learning algorithm. The proposed method is validated with several benchmark examples, and the results show that the ensemble of multiple surrogate models is very efficient for estimating failure probability (> 10(-4)) of complex system with less computational costs than the traditional single surrogate model.