Robust active learning framework for structural reliability analysis using uncertainty quantification and flexible meta-model

Abstract

Achieving highly accurate results in assessing structural reliability with reduced time complexity is an active and challenging engineering topic, especially when working with complex real-world structures. One of the most prominent methods addressing this issue is AK-MCS, which adaptively updates the Kriging-based surrogate model for forecasting structures’ behavior. While the Kriging model’s performance may be limited compared to multiple new machine learning models, it remains dominant in reliability analysis thanks to its distinctive ability to assess uncertainty along with prediction results. This ability is necessary to determine the potential candidate sample in the active learning procedure. This study proposes a novel method, dubbed AUQ-meta, that allows us to leverage the recent powerful machine learning models while still being able to produce uncertainty quantification. The AUQ-meta method consists of three main components: a machine learning-based predictor, a quantile regression module for uncertainty assessment, and an adaptive reliability analysis process. The effectiveness and efficiency of the proposed method are consistently validated through six examples with high dimensionality, nonlinearity, and time-varying elements. In addition, comparison and sensitivity studies are carried out, providing further insights into the AUQ-meta method’s operational mechanism.