Towards the definition of fragility curves via machine learning

The seismic vulnerability assessment of historical buildings, particularly churches, is crucial for risk mitigation in seismic-prone regions like Italy. The study focuses on the typological characterization of existing Italian churches with the aim of deriving fragility curves through data-driven methods. Based on an available extensive dataset (Da.D.O. – Database Danno Osservato), a subset of single-nave churches was identified based on geometric and architectural criteria. The paper presents a detailed typological analysis of single-nave churches spread along Italian territory with the aim of evaluating the influence of parameters on seismic vulnerability. The results of these analyses will be used to generate a dataset suitable for future machine learning applications aimed at predicting fragility parameters based on the execution of a number of nonlinear static analyses. The research presents the typological framework and the methodology adopted for model selection, parameter variation, and the setup for developing a comprehensive fragility dataset. While the application of machine learning is beyond the scope of this study, the groundwork laid here is intended to support the integration of data-driven techniques in the seismic risk assessment of heritage buildings.