Adaptive Machine Learning-Driven Multi-Fidelity Stratified Sampling for Failure Analysis of Nonlinear Stochastic Systems

TL;DR

This paper introduces an adaptive multi-fidelity stratified sampling method using machine learning metamodels to efficiently estimate small failure probabilities in complex nonlinear stochastic systems, significantly reducing computational costs.

Contribution

It develops a novel adaptive multi-fidelity sampling scheme with deep learning-based metamodels for efficient failure probability estimation in nonlinear stochastic systems.

Findings

Accurately estimates failure probabilities with fewer model evaluations.

Achieves significant computational savings over traditional methods.

Demonstrates effectiveness on a high-rise steel building subjected to wind excitation.

Abstract

Existing variance reduction techniques used in stochastic simulations for rare event analysis still require a substantial number of model evaluations to estimate small failure probabilities. In the context of complex, nonlinear finite element modeling environments, this can become computationally challenging-particularly for systems subjected to stochastic excitation. To address this challenge, a multi-fidelity stratified sampling scheme with adaptive machine learning metamodels is introduced for efficiently propagating uncertainties and estimating small failure probabilities. In this approach, a high-fidelity dataset generated through stratified sampling is used to train a deep learning-based metamodel, which then serves as a cost-effective and highly correlated low-fidelity model. An adaptive training scheme is proposed to balance the trade-off between approximation quality and computational demand associated with the development of the low-fidelity model. By integrating the low-fidelity outputs with additional high-fidelity results, an unbiased estimate of the strata-wise failure probabilities is obtained using a multi-fidelity Monte Carlo framework. The overall probability of failure is then computed using the total probability theorem. Application to a full-scale high-rise steel building subjected to stochastic wind excitation demonstrates that the proposed scheme can accurately estimate exceedance probability curves for nonlinear responses of interest, while achieving significant computational savings compared to single-fidelity variance reduction approaches.