Seismic fragility analysis using stochastic polynomial chaos expansions

TL;DR

This paper introduces a stochastic polynomial chaos expansion method to efficiently estimate seismic fragility models within performance-based earthquake engineering, reducing computational costs and improving accuracy over traditional approaches.

Contribution

The study develops a novel surrogate modeling approach using stochastic polynomial chaos expansions to accurately estimate seismic fragility functions conditioned on stochastic ground motion parameters.

Findings

The proposed method outperforms existing techniques in estimating conditional EDP distributions.

It significantly reduces computational costs compared to full-scale Monte Carlo simulations.

Numerical case studies validate the effectiveness and accuracy of the new approach.

Abstract

Within the performance-based earthquake engineering (PBEE) framework, the fragility model plays a pivotal role. Such a model represents the probability that the engineering demand parameter (EDP) exceeds a certain safety threshold given a set of selected intensity measures (IMs) that characterize the earthquake load. The-state-of-the art methods for fragility computation rely on full non-linear time-history analyses. Within this perimeter, there are two main approaches: the first relies on the selection and scaling of recorded ground motions; the second, based on random vibration theory, characterizes the seismic input with a parametric stochastic ground motion model (SGMM). The latter case has the great advantage that the problem of seismic risk analysis is framed as a forward uncertainty quantification problem. However, running classical full-scale Monte Carlo simulations is intractable because of the prohibitive computational cost of typical finite element models. Therefore, it is of great interest to define fragility models that link an EDP of interest with the SGMM parameters -- which are regarded as IMs in this context. The computation of such fragility models is a challenge on its own and, despite few recent studies, there is still an important research gap in this domain. This study tackles this computational challenge by using stochastic polynomial chaos expansions to represent the statistical dependence of EDP on IMs. More precisely, this surrogate model estimates the full conditional probability distribution of EDP conditioned on IMs. We compare the proposed approach with some state-of-the-art methods in two case studies. The numerical results show that the new method prevails its competitors in estimating both the conditional distribution and the fragility functions.