PSI - Issue 78

Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 78 (2026) 845–851

© 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 Abstract Reliable prediction of the seismic fragility of bridges is critical for structural safety and the prioritization of risk mitigation actions. This work explores the application of Machine Learning (ML) techniques to estimate key fragility parameters, median demand (μ), dispersion (β ), and ultimate displacement (d u ), from a reduced set of structural descriptors. A database of 25 simply supported bridges located along the A19 Catania – Palermo motorway in Sicily is developed using nonlinear static analyses, and fragility curves are derived via Cloud Analysis. Several supervised regression models, including Gaussian Process Regression (GPR), Random Forest, SVR, and others, are trained to map structural features to fragility parameters. Among them, GPR consistently shows the best performance. The resulting ML-based fragility curves exhibit excellent agreement with those obtained through conventional methods, confirming the validity of the approach. The study demonstrates the potential of ML to support rapid seismic screening of bridge inventories, significantly reducing computational costs while maintaining reliability. Future developments will aim to expand the methodology to different structural typologies and incorporate epistemic uncertainties in the learning process. XX ANIDIS Conference Sensitivity analysis of different machine learning models in the seismic response assessment of bridges Gianluca Quinci a, *, Ignazio Casiraro b , Marinella Fossetti c , Hoang Nam Phan d , 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 d Faculty of Road and Bridge Engineering, The University of Danang, University of Science and Technology, Danang 550000, Vietnam

* Corresponding author. Tel.: +39 3387633874;. E-mail address: gianluca.quinci@uniroma3.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.2025.12.108