Portfolio allocation strategy for active learning Kriging-based structural reliability analysis

Recently, numerous studies have focused on structural reliability analysis, with the Kriging-based active learning method being particularly popular. A variety of Kriging-based learning functions have been proposed, and shown to perform well in various tasks. However, no single learning function has been demonstrated to consistently outperformed the others in all tasks, and selecting the most appropriate learning function for a given task remains a challenge in engineering applications. In this paper, inspired by the multi-armed bandit strategy, a portfolio allocation of different learning functions is proposed to resolve the issue of selecting a single one, where the better learning functions are selected online according to their past performance. Finally, three classical numerical examples and two engineering applications are adopted to validate the effectiveness of the proposed method.