PSI - Issue 84

Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 84 (2026) 1326–1333

© 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 Keywords: Railway bridges; Dynamic behaviour; Moving loads; Parametric workflow; Finite element analysis; Time-history response; Dynamic amplification. 1. Introduction The global expansion of high-speed rail lines entails the need to adapt existing infrastructure, with particular attention to bridges and viaducts. Abstract This paper presents a fully parametric and automated workflow for the dynamic assessment of railway bridges subjected to moving train loads. Traditional approaches often rely on oversimplified structural models, limiting their applicability to complex and actual geometries. The proposed method enables the flexible generation of different bridge typologies and their direct conversion into finite element models suitable for dynamic analysis. Structural parameters such as geometry, material properties, and modal settings can be modified within a unified visual scripting environment. Train axle loads are defined through a data-driven input file specifying inter-axle distances and weights, which are automatically converted into time-dependent load functions for analysis. The paper details each stage of the workflow, provides an application to a real case study for its preliminary validation, and discusses the implications for structural safety assessments and design practices. III Fabre Conference: Existing Bridges, Viaducts, and Tunnels: Research, Innovation, and Applications An automated workflow for the dynamic behavior of railway bridges Lorenzo Sangiuliano a, *, Agnese Natali a , Giuseppe Chellini a , Walter Salvatore a a Dipartimento di Ingegneria Civile e Industriale, Università di Pisa, Largo Lucio Lazzarino, 56122, Pisa, Italia

* Corresponding author. Tel.: +39-3339857038. E-mail address: lorenzo.sangiuliano@phd.unipi.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.2026.06.169