PSI - Issue 84

Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 84 (2026) 199–206

© 2026 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) Abstract Accurate assessment of bridge vulnerability to seismic events is essential for ensuring structural resilience and guiding effective risk management strategies. This study investigates the use of Machine Learning (ML) models to predict the main parameters defining seismic fragility curves— namely, the median seismic demand (μ), dispersion (β), and ultimate displacement capacity (du)—based on a limited number of structural input variables. A case study involving 25 simply supported bridges along the A19 highway in Sicily serves as the basis for a comprehensive dataset, generated through nonlinear static analyses. Fragility functions are constructed via the Cloud Analysis technique. Several supervised learning algorithms, including Gaussian Process Regression (GPR), Support Vector Regression (SVR), and ensemble-based approaches, are trained to infer fragility parameters from structural features. Among the tested models, GPR achieves the highest accuracy and generalization performance. The fragility curves derived from ML predictions show strong consistency with those obtained from traditional analytical methods, validating the approach. The proposed methodology enables efficient seismic vulnerability screening of bridge stocks with a considerable reduction in computational demand. III Fabre Conference: Existing Bridges, Viaducts, and Tunnels: Research, Innovation, and Applications Data-driven fragility assessment of bridges using Machine-Learning techniques Gianluca Quinci. a,* , Ignazio Casiraro b , Marinella Fossetti c , Fabrizio Paolacci a a Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University Via Vito Volterra 62, 00146, Rome, Italy b Department of Civil Engineering and Architecture, University of Catania, Via Santa Sofia 64, 95125, Catania, Italy c Department of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100, Enna, Italy

Peer-review under responsibility of the scientific committee of the Conference Keywords: Machine learning; Vulnerability Assessment; Fragility Curve; Bridges

* Corresponding author. Tel.: +39 3387633874;. E-mail address: gianluca.quinci@uniroma3.it

2452-3216 © 2026 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 the scientific committee of the Conference 10.1016/j.prostr.2026.06.027