Optimized active learning Kriging reliability based assessment of laterally loaded pile groups modeled using random finite element analysis

Abstract

This paper presents an optimization of the Active learning Kriging (AK) reliability assessment of laterally loaded pile groups modeled using random nonlinear finite element (FE) where the spatial variability in the soil response is discretized using three dimensional (3D) random fields. The input functions of the AK including the regression, correlation, and the learning functions (called AK configuration herein) are optimized for four AK based reliability estimation techniques: AK Monte Carlo simulation (MCS), AK first order reliability method (FORM), AK importance sampling (IS), and AK self-normalizing importance sampling (LS). The optimal AK configurations are obtained using an extensive parametric analysis (4320 reliability assessment analyses). Two rational-based methods are adopted to propose the optimal AK configurations: accuracy-based priority ranking and consistency-based priority ranking. Summary tables of the optimal AK configurations are included in the paper. The proposed optimal AK configurations can be used by engineers to assess the reliability of pile group-soil interaction problems using AK and random FE analysis.