PSI - Issue 84

Laura Ierimonti et al. / Procedia Structural Integrity 84 (2026) 959–966

966

Bayesian inference and model class selection based on the BIC demonstrated that reduced-order surrogate models consistently outperform highly parameterized alternatives. The results indicate that the available vibration data primarily support local stiffness variations, while excessive model complexity leads to parameter non-identifiability and reduced statistical efficiency. Stable model rankings and concentrated posterior distributions confirm the robustness and interpretability of the proposed framework. It is worth noticing that the surrogate models were calibrated using one FE representation of the bridge (FEM A ), while damage scenarios were generated using a separate, independently calibrated model (FEM B ). This design choice reflects the practical absence of prior information about damaged states and mitigates potential bias arising from model self-consistency, thereby enhancing the robustness of the proposed methodology. Acknowledgements The authors acknowledge ANAS S.p.A. for access to monitoring data and technical documentation of the Volumni Bridge. This work was supported by the FABRE–ANAS 2021–2026 research program under the FABRE Research Consortium (www.consorziofabre.it/en).. The views expressed are those of the authors and not necessarily of the funding bodies. References Alkayem, N. F., Cao, M., Zhang, Y., Bayat, M., & Su, Z. (2018). Structural damage detection using finite element model updating with evolutionary algorithms: A survey. Neural Computing and Applications, 30(2), 389–411. Anas S.p.A., Monitoraggio di ponti e viadotti tramite sensori, 2025, Last accessed: 10 2025. Brincker, R., & Ventura, C. (2015). Introduction to Operational Modal Analysis. John Wiley & Sons. Cabboi, A., Magalhães, F., Gentile, C., & Cunha, Á. (2017). Automated modal identification and tracking: Application to an iron arch bridge. Structural Control and Health Monitoring, 24(1), e1854. García-Macías, E., Ubertini, F., 2020. MOVA/MOSS: Two integrated software solutions for comprehensive Structural Health Monitoring of structures. Mechanical Systems and Signal Processing 143, 106830. García-Macías, E., Venanzi, I., Ubertini, F., 2020. Metamodel-based pattern recognition approach for real-time identification of earthquake-induced damage in historic masonry structures. Automation in Construction 120. Ghosh, J.K., Delampady, M., Samanta, T., 2006. An Introduction to Bayesian Analysis: Theory and Methods. Springer-Verlag, New York. He, Z., Li, W., Salehi, H., Zhang, H., Zhou, H., & Jiao, P. (2022). Integrated structural health monitoring in bridge engineering. Automation in Construction, 136, 104168. Ierimonti, L., Cavalagli, N., Venanzi, I., García-Macías, E., Ubertini, F., 2021. A transfer Bayesian learning methodology for structural health monitoring of monumental structures. Engineering Structures 247, 113089. Lam, H.F., Yang, J.H., Au, S.K., 2018. Markov chain Monte Carlo-based Bayesian method for structural model updating and damage detection. Structural Control and Health Monitoring 25(4), e2140. Li, Q., Ni, P., Du, X., Han, Q., Xu, K., & Bai, Y. (2025). Bayesian updating using accelerated Hamiltonian Monte Carlo with gradient‑enhanced Kriging model. Computers & Structures, 307, 107598. Magalhães, F., & Cunha, Á. (2011). Explaining operational modal analysis with data from an arch bridge. Mechanical Systems and Signal Processing, 25(5), 1431–1450. Rainieri, C., & Fabbrocino, G. (2014). Operational Modal Analysis of Civil Engineering Structures (Vol. 142). Springer, New York. Tomassini, E., Centofanti, G., Chellini, G., García-Macías, E., Lepori, L., Mannella, P., Salvatore, W., Ubertini, F., 2025. Key findings from long term operational modal analysis of a landmark steel arch bridge in Italy. Structures 82, 110436.Tomassini, E., García-Macías, E., Ubertini, F., 2025. Model-based transfer learning for real-time damage assessment of bridge networks. Automation in Costruction 180, 106581. Quqa, S., Landi, L., Diotallevi, P.P., 2021. Automatic identification of dense damage-sensitive features in civil infrastructure using sparse sensor networks. Automation in Construction 128, 103740. Zhang, J., Maes, K., De Roeck, G., Lombaert, G., 2021. Model updating for a large multi-span quasi-periodic viaduct based on free wave characteristics. Journal of Sound and Vibration 506, 116161. Hernández-González, Israel Alejandro, and Enrique García-Macías. "Towards a comprehensive damage identification of structures through populations of competing models." Engineering with Computers 40.5 (2024): 3157-3174.