Effect of learning function on reliability analysis of geotechnical engineering systems using adaptive Bayesian compressive sensing and Monte Carlo simulation

This study explored the effect of learning function on reliability analysis of geotechnical engineering system using adaptive Bayesian compressive sensing (ABCS) and Monte Carlo simulation (MCS) (ABCS-MCS). The ABCS-MCS method can provide both response prediction at an unsampled point and quantify explicitly the associated prediction uncertainty. It relies on a learning function to adaptively determine the minimum number of sampling points and corresponding sampling locations for achieving a target accuracy of reliability analysis. Therefore, learning function plays an important role in ABCS-MCS, and its learning criteria and stopping condition directly affect the accuracy and efficiency of reliability analysis. Four different learning functions are investigated together with ABCS-MCS. A comparative study using these four learning functions in ABCS-MCS is illustrated using a two-layered cohesive slope reliability analysis problem. Results show that ABCS-MCS combined with U-learning function has the highest accuracy, efficiency, and robustness for reliability analysis. © 2023 by the American Society of Civil Engineers.