Seismic fragility curves fitting revisited: ordinal regression models and their generalization

TL;DR

This paper explores the use of various ordinal regression models to improve seismic fragility curve fitting, offering a flexible alternative to traditional log-normal models for better seismic risk assessment.

Contribution

It introduces and compares multiple ordinal regression approaches, including extensions with heteroscedasticity and category-specific effects, applied to real earthquake damage data.

Findings

Sequential model with category-specific effects performs best in cross-validation.

Ordinal regression models provide more flexible fragility curve fitting.

Differences observed in damage probability predictions across models.

Abstract

This study revisits the modeling of seismic fragility curves by applying ordinal regression models, offering an alternative to the commonly used log-normal distribution function. It compares various ordinal regression approaches, including Cumulative, Sequential, and Adjacent Category models, along with extensions that account for category-specific effects and variance heterogeneity. The methodologies are applied to bridge damage data from the 2008 Wenchuan earthquake, using both frequentist and Bayesian inference methods, with model diagnostics conducted using surrogate residuals. The analysis examines eleven models, from basic forms to those incorporating heteroscedastic extensions and category-specific effects. Based on leave-one-out cross-validation, the Sequential model with category-specific effects performs well compared to traditional Cumulative probit models. The results indicate differences in damage probability predictions between the models, suggesting the potential for more flexible fragility curve modeling techniques to improve seismic risk assessments. This study highlights the importance of continued evaluation of existing methods to enhance the predictive accuracy and applicability of seismic fragility models in performance-based earthquake engineering.