PSI - Issue 78

Available online at www.sciencedirect.com

ScienceDirect

Procedia Structural Integrity 78 (2026) 1167–1174

XX ANIDIS Conference Assessment and strengthening of Gerber saddles in RC bridges

Valentina Picciano a, *, Giuseppe Santarsiero a , Angelo Masi a a Department of Engineering, University of Basilicata, via dell'Ateneo Lucano 10, Potenza 85100, Italy

© 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: Bridges; Gerber saddles; failure modes; strengthening; machine learning; nonlinear analysis. Abstract Gerber saddles represent critical structural elements in reinforced concrete bridges, as they are subjected to high stress states and degradation phenomena that may compromise their integrity and, in severe cases, lead to the collapse of the entire structure. Their vulnerability is acknowledged in the Italian Guidelines for the Classification and Risk Management of Existing Bridges. This study summarises and presents research carried out on Gerber saddles, beginning with an analysis of their structural behaviour and extending to the assessment of various available strengthening techniques, to identify optimised strategies that consider structural, practical, and economic aspects, all within a framework of enhancing safety and infrastructure resilience. A comprehensive database of experimental tests from the literature has been collected through a critical review of the state of the art, which proved useful in identifying the main failure mechanisms in relation to construction details. The comparative evaluation of strengthening methods has led to the development of a logical framework for selecting the most appropriate interventions, also assessing operational impacts and on-site feasibility. Particular attention was given to the use of external post-tensioning, which has proven to be effective, cost-efficient, and minimally invasive. Through nonlinear numerical analyses and application to case studies, a design methodology was proposed that allows for the optimisation of interventions based on pre-tensioning levels, significantly improving the structural performance of Gerber saddles. Moreover, the integration of Machine Learning techniques has highlighted the potential of such tools in enabling fast and reliable predictions of load-carrying capacity, providing practical support for structural safety assessments in professional engineering practice. Therefore, the research presented offers helpful tools for engineering applications, contributing to improved maintenance management and intervention planning on existing infrastructures.

* Corresponding author. E-mail address: valentina.picciano@unibas.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.2025.12.149