Reference prior for Bayesian estimation of seismic fragility curves

TL;DR

This paper introduces a new objective Bayesian prior, the Jeffreys prior, for estimating seismic fragility curves from limited binary data, improving robustness and avoiding degenerate estimates.

Contribution

It derives the first Jeffreys prior for Bayesian seismic fragility estimation with binary data, ensuring proper posterior distribution and more reliable results.

Findings

Jeffreys prior leads to proper and boundary-vanishing posterior distributions.

Using Jeffreys prior avoids degenerate fragility curves.

Numerical case studies validate theoretical advantages.

Abstract

One of the key elements of probabilistic seismic risk assessment studies is the fragility curve, which represents the conditional probability of failure of a mechanical structure for a given scalar measure derived from seismic ground motion. For many structures of interest, estimating these curves is a daunting task because of the limited amount of data available; data which is only binary in our framework, i.e., only describing the structure as being in a failure or non-failure state. A large number of methods described in the literature tackle this challenging framework through parametric log-normal models. Bayesian approaches, on the other hand, allow model parameters to be learned more efficiently. However, the impact of the choice of the prior distribution on the posterior distribution cannot be readily neglected and, consequently, neither can its impact on any resulting estimation. This paper proposes a comprehensive study of this parametric Bayesian estimation problem for limited and binary data. Using the reference prior theory as a cornerstone, this study develops an objective approach to choosing the prior. This approach leads to the Jeffreys prior, which is derived for this problem for the first time. The posterior distribution is proven to be proper with the Jeffreys prior but improper with some traditional priors found in the literature. With the Jeffreys prior, the posterior distribution is also shown to vanish at the boundaries of the parameters' domain, which means that sampling the posterior distribution of the parameters does not result in anomalously small or large values. Therefore, the use of the Jeffreys prior does not result in degenerate fragility curves such as unit-step functions, and leads to more robust credibility intervals. The numerical results obtained from different case studies-including an industrial example-illustrate the theoretical predictions.