PSI - Issue 78

Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 78 (2026) 710–717

© 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of XX ANIDIS Conference organizers Keywords: Existing bridges; Reinforced concrete; Seismic fragility assessment; Taxonomy; Machine Learning Abstract The evaluation of the seismic vulnerability of existing bridge portfolios is of great concern for transport network managers especially in earthquake-prone countries. The not-negligible number of bridges designed in the past without adequate anti-seismic requirements, the poor structural knowledge of these structures and the material degradation pose challenges in the evaluation. This study proposes a framework for large scale seismic vulnerability assessment of multi-span reinforced concrete girder (RC) bridges considering knowledge-based uncertainties and a novel bridge taxonomy based on risk attributes collected in the prioritization steps of the multi-level approach of the Italian guidelines. It is based on subsequent steps that involve the input of basic knowledge data (e.g., the maximum bridge span length), the simulation of knowledge-based uncertainties, and simplified seismic analysis. An Extreme Gradient Boosting Machine Learning algorithm is investigated to define a binary classificatory able to predict whether the pier failure occurs and is used for the seismic fragility assessment through generated fragility curves. A case-study section demonstrates the application of the framework in the case of circular RC piers showing promising results for seismic risk-informed prioritization of bridges and suggesting the possibility of extending it to other bridge-pier systems. XX ANIDIS Conference Machine learning based framework for the seismic fragility assessment of reinforced concrete bridges Mirko Calò a, *, Vincenzo Di Mucci a , Andrea Nettis a , Sergio Ruggieri a , Andrea Dall’Asta b , Giuseppina Uva a a DICATECH Department, Polytechnic University of Bari, Bari, Italy b School of Science and Technology, University of Camerino, Camerino, Italy

* Corresponding author. E-mail address: mirko.calo@poliba.it

2452-3216 © 2025 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of XX ANIDIS Conference organizers 10.1016/j.prostr.2025.12.091