PSI - Issue 62

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia -- (2024) _ – _

www.elsevier.com/locate/procedia

ScienceDirect

Procedia Structural Integrity 62 (2024) 701–709

II Fabre Conference – Existing bridges, viaducts and tunnels: research, innovation and applications (FABRE24) On the use of machine learning for fragility analysis of multi-span girder bridges Fabio Parisi a , Andrea Nettis b,* , Giuseppina Uva b a Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, Italy, e-mail: fabio.parisi@poliba.it b Department of Civil, Environmental, Land, Building Engineering and Chemistry, Polytechnic University of Bari, Bari, Italy, e-mail: {andrea.nettis , giuseppina.uva}@poliba.it Abstract Companies and enterprises managing the transportation networks are in charge of performing seismic risk assessments for a sensible number of bridges designed, over the past decades, without fulfilling requirements of anti-seismic codes. Recent research studies have focused on leveraging machine learning strategies to enhance the efficiency of probabilistic seismic assessments. This study tackles the integration of machine learning algorithms in the case of bridge-specific applications to predict probabilistic seismic demand models for substructure components such as piers, accounting for knowledge-based uncertainties. The machine-learning-based methodology employed builds on the results of nonlinear time history analyses and considers features related to seismic excitation and structural parameters. Random Forest algorithm is employed to investigate the methodology for a multi-span simply supported girder bridge, which is a representative example of the most prevalent bridge class in Europe. Aiming to reduce the need for extensive nonlinear time history analyses in the risk assessment of bridges belonging to this specific class, the methodology proposed shows promising potential highlighted in the results. © 2024 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 Scientific Board Members Keywords: fragility analysis, RC bridges, seismic risk, machine learning, feature importance 1. Introduction An inadequate seismic response of bridges to strong earthquakes can affect the serviceability of entire transport networks. Therefore, significant direct and indirect losses for urbanised contexts can be induced. Countries as Europe can potentially strongly suffer from this issue, being most of the existing bridges designed in the past © 2024 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 Scientific Board Members

2452-3216 © 2024 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 Scientific Board Member s

2452-3216 © 2024 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 Scientific Board Members 10.1016/j.prostr.2024.09.097