Exploring Passive and Active Metamodeling-Based Reliability Analysis Methods for Soil Slopes: A New Approach to Active Training

Abstract

For geotechnical systems involving complex behaviors and significant uncertainties, metamodeling-based reliability methods based on fixed designs of experiment (passive metamodeling methods) are frequently applied. However, because of the passive nature of these methods, they do not guarantee accurate probability estimates. This issue also persists in estimates obtained from active metamodeling methods (metamodeling methods that update the design of experiment iteratively) if a global stopping criterion or a nonoptimal learning function is used for the type of problem. This study highlights the importance of addressing these issues by investigating an example of failure analysis of soil slopes and concludes that the stopping criteria for development and refinement of metamodels need to be objective and locally defined with respect to the limit state function (LSF) and with a direct link to the accuracy of the reliability estimates. To address these challenges, this study established an effective sampling region that optimally ignores candidate design samples with weak probability densities. This enabled the development of an effective learning function that facilitates the active learning process. Moreover, an analytical upper bound for the error in failure probability estimation, proposed by the authors, was adopted here to arrive at a local and objective stopping criterion. This method was applied on two soil slopes, the stability of which was evaluated by the strength reduction method (SRM) in FLAC3D. The results highlight that passive and active methods with global stopping criteria cannot guarantee an accurate estimate of the failure probability. Furthermore, the developed method can considerably reduce the computational demands while achieving accurate estimates of failure probabilities of soil slopes.