PSI - Issue 84

F. Foria et al. / Procedia Structural Integrity 84 (2026) 645–652

652

5. Conclusions The activities described in this paper are part of the broader MIREB (Management and Identification of the Risk for Existing Bridges) framework, which ETS is developing to integrate monitoring, numerical modelling, and risk management into unified digital workflows, following the approach already adopted in previous frameworks such as MIRET for tunnels and MIRETS for hydrogeological risk mapping. The algorithm described is integrated into the broader context of the bridge monitoring system. After the procedure processes the acquired signals and extracts the dynamic parameters, anomalies in the structural behaviour can be detected [9], and alarm signals and safety procedures may be activated. A distinctive feature of the system is its interface with FEM numerical models, enabling the updating of structural models based on real data and the execution of engineering-level safety assessments under current conditions. This approach goes beyond simple monitoring by integrating dynamic observation with structural modelling to provide a more robust and timely evaluation of bridge health. It also serves as the starting point for creating a Digital Twin of the structure and for automated management of the entire control process. The MIREB framework will extend these capabilities by incorporating inspection data alongside monitoring results, numerical analyses, and management strategies into fully digitalized workflows, as already implemented in MIRET and MIRETS. Future developments will focus on strengthening risk management functionalities, industrializing the algorithms for greater efficiency and scalability, and enhancing interoperability with BIM platforms and early-warning systems to support preventive maintenance and lifecycle-based infrastructure management. 6. Bibliography [1] K. Zhou, Q.-S. Li, X.-L. Han, Modal identification of civil structures via stochastic subspace algorithm with Monte Carlo–based stabilization diagram, J. Struct. Eng. 148 (6) (2022) 04022066 [2] M. M. Rosso, A. Aloisio, J. Parol, G. C. Marano, G. Quaranta, Intelligent automatic operational modal analysis, Mechanical Systems and Signal Processing, Volume 201, 2023, 110669, ISSN 0888-3270. [3] Rainieri, C. & Fabbrocino, Giovanni. (2014). Influence of model order and number of block rows on accuracy and precision of modal parameter estimates in stochastic subspace identification. International Journal of Lifecycle Performance Engineering. 1. 317. 10.1504/IJLCPE.2014.064099. [4] F. Foria, et al. “Artificial intelligence and image processing in the MIRET approach for the water detection and integrated geotechnical management of existing mechanized tunnels: Methodology, algorithm and case study”. The Evolution of Geotech – 25 Years of Innovation. Taylor & Francis Group. 2021. [5] F. Foria, G. Miceli, M. Calicchio, M. Brichese “Decarbonization and climate change analysis of tunnels in an Asset Management framework through MIRET”. Procedia Structural Integrity. Elsevier. 2024. [6] F. Foria, G. Miceli, A. Tamburini, F. Villa, A. Rech, F. Epifani “Application of Spatial Multi-Criteria Analysis (SMCA) to assess rockfall hazard and plan mitigation strategies along long infrastructures”. Proceedings of ISRM EUROCK. ISRM. 2021. [7] R. Romanello, E. Miraglia, G. Miceli, S. Gazzo, L. Contrafatto, M. Cuomo, S. Scalisi, New advanced monitoring systems of Bridges with Actionable Real Time Sensor Data, Procedia Structural Integrity, Volume 62, 2024, Pages 856-863, ISSN 2452-3216 [8] Rainieri, C. and Fabbrocino, G. (2014) Operational Modal Analysis of Civil Engineering Structures: An introduction and guide for applications. New York: Springer. [9] Scionti, Marco, et al. "Tools to improve detection of structural changes from in-flight flutter data." Proceedings of the Eighth International Conference on recent advances in Structural Dynamics. 2003.