Importance sampling based active learning for parametric seismic fragility curve estimation

TL;DR

This paper introduces an active learning method based on adaptive importance sampling for efficient seismic fragility curve estimation, addressing uncertainties and reducing computational costs in complex numerical models.

Contribution

It presents a rigorous mathematical framework for active learning in seismic fragility estimation, including theoretical properties and convergence criteria.

Findings

Method achieves accurate estimates with fewer model evaluations.

Theoretical guarantees of consistency and asymptotic normality.

Effective performance demonstrated on complex analytical and industrial cases.

Abstract

The key elements of seismic probabilistic risk assessment studies are the fragility curves which express the probabilities of failure of structures conditional to a seismic intensity measure. A multitude of procedures is currently available to estimate these curves. For modeling-based approaches which may involve complex and expensive numerical models, the main challenge is to optimize the calls to the numerical codes to reduce the estimation costs. Adaptive techniques can be used for this purpose, but in doing so, taking into account the uncertainties of the estimates (via confidence intervals or ellipsoids related to the size of the samples used) is an arduous task because the samples are no longer independent and possibly not identically distributed. The main contribution of this work is to deal with this question in a mathematical and rigorous way. To this end, we propose and implement an active learning methodology based on adaptive importance sampling for parametric estimations of fragility curves. We prove some theoretical properties (consistency and asymptotic normality) for the estimator of interest. Moreover, we give a convergence criterion in order to use asymptotic confidence ellipsoids. Finally, the performances of the methodology are evaluated on analytical and industrial test cases of increasing complexity.