Uncertainty quantification for seismic response using dimensionality reduction-based stochastic simulator

TL;DR

This paper presents a stochastic simulator that combines dimensionality reduction and probabilistic modeling to efficiently quantify seismic uncertainties in structural responses, accommodating high-dimensional inputs and nonlinear analyses.

Contribution

It extends dimensionality reduction-based surrogate modeling to handle high-dimensional seismic data and nonlinear responses without assuming specific response distributions.

Findings

Accurately predicts multivariate seismic responses.

Effectively quantifies uncertainties including correlations.

Demonstrates efficiency on finite element building models.

Abstract

This paper introduces a stochastic simulator for seismic uncertainty quantification, which is crucial for performance-based earthquake engineering. The proposed simulator extends the recently developed dimensionality reduction-based surrogate modeling method (DR-SM) to address high-dimensional ground motion uncertainties and the high computational demands associated with nonlinear response history analyses. By integrating physics-based dimensionality reduction with multivariate conditional distribution models, the proposed simulator efficiently propagates seismic input into multivariate response quantities of interest. The simulator can incorporate both aleatory and epistemic uncertainties and does not assume distribution models for the seismic responses. The method is demonstrated through three finite element building models subjected to synthetic and recorded ground motions. The proposed method effectively predicts multivariate seismic responses and quantifies uncertainties, including correlations among responses.