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00925njm 22002777a 4500 dc Garcia Ramonda, Larisa author Bogoevska, Simona author Reuland, Yves author Martakis, Panagiotis author Dertimanis, Vasilis author Chatzi, Eleni author 2025-10-11 This material may be downloaded for personal use only. Any other use requires prior permission of the American Society of Civil Engineers. This material may be found at https://ascelibrary.org/doi/abs/10.1061/JSENDH.STENG-15086 In addressing the challenge of ageing infrastructure, continuous structural monitoring has figured prominently in the development of tools for risk management and life-cycle prognostic strategies. However, a primary challenge lies in robustly quantifying structural condition using, typically indirect, monitoring observations. In recent years, a vast number of various data-driven or hybrid analysis methods have been proposed, targeting different levels of the so-called Rytter’s hierarchy of damage identification. The more advanced identification tasks, relating to a more precise characterization (e.g., location and quantity) of damage are nontrivial to address. A primary difficulty in this respect relates to lack of labelled data corresponding to predefined damage states of the structure of interest. This implies that for most practical contexts, such structural identification ought to be achieved in an unsupervised manner. This work presents a new full-scale bridge experimental benchmark which can serve as a case study for verification and validation of damage identification schemes. The Ponte Moesa Campagnola (PMC) benchmark structure, which was decommissioned in 2019, represents a typical bridge structure of the Swiss Roadway Network. The bridge was subjected to a 4-day monitoring campaign, during which controlled damage progression scenarios were implemented. The monitored quantities comprised a multimodal mix of both acceleration and strain information, continually recorded during the 4-day campaign. The reported results demonstrate the potential of this data set to serve for structural identification and damage detection (DD) purposes. To this end, we present—merely as a viability study—the successful implementation of three damage-sensitive features (DSFs). The goal was to describe the data set and introduce it for further study, testing, and validation of emerging DD algorithms tailored for full-scale structural health monitoring (SHM) utilization. The authors greatly acknowledge the support of the Swiss Federal Roads Ofice (ASTRA/FEDRO) for conception and support of this project. We further thank ETH Zürich IBK Bauhalle team, and Dominik Werne and Christoph Gisler in particular, for their strong support during the prepara545 tion and execution of the measurements. In addition, the support of Dr. Konstantinos Vlachas, Dr. Lingzhen Li and Dr. Rachele Zaccherini, member of the Chair of Structural Mechanics and Monitoring at ETH Zürich, is thankfully acknowledged. The first author gratefully acknowledges the European Union – NextGeneration EU for the financial support through the Margarita Salas postdoctoral grant (CG/2021/03/23). 11 - Ciutats i Comunitats Sostenibles 9 - Indústria, Innovació i Infraestructura 13 - Acció per al Clima Postprint (published version) Àrees temàtiques de la UPC::Enginyeria civil::Materials i estructures::Càlcul d'estructures Structural condition Hybrid analysis methods Damages Bridge structures Ponte Moesa Campagnola: a bridge benchmark for structural identification under controlled damage progression