Metamodelling for geotechnical reliability analysis with noisy and incomplete models

Abstract

A kriging-based metamodelling approach for analysing the structural reliability of a sheetpile wall in a dyke is formulated. This specific problem is characterised by high target reliabilities ((Formula presented.)) in combination with a noisy and incomplete numerical model response. Starting from the original formulation of active learning kriging-based Monte Carlo simulation (AK-MCS), a robust two-stage metamodel framework is formulated in combination with adaptive multiple importance sampling, Gaussian process classification and kernel enhancements. Learning functions and convergence criteria are revised to maintain consistency with the metamodel enhancements. The developed metamodel is applied in the reliability analysis of a soil-structure interaction problem involving a sheetpile wall in a dyke body, which is representative of a class of problems encountered in engineering practice. Low dimensional example studies demonstrate the workings of the model and give insight into the model response. Full probabilistic analyses are then performed to estimate the probabilities of structural failure in a reliability updating context. The results show that after several necessary enhancements of the classical formulations, metamodelling approaches can be used successfully in combination with noisy and incomplete computational models as are often encountered in geotechnical engineering practice.