PSI - Issue 84

Laura Dieci et al. / Procedia Structural Integrity 84 (2026) 591–598

598

( ) ( ) 1.012 0.985

Table 3. Comparison between experimental and numerical vertical deflections. Location

Relative error (%)

Gerber saddle

2.67 -2.42

Midspan

1.902

1.948

6. Conclusions This study investigates the structural behavior of the “Ponte delle Grazie” by combining experimental testing and numerical modeling. Dynamic and static tests were performed to characterize the in-service response and to provide the experimental reference for model calibration, supplying a coherent set of modal parameters and load-induced deflections. The finite element model developed in this work offers a detailed representation of the deck and its main structural components. Its calibration is carried out by updating a set of mechanical parameters, enabling the model to capture the essential features of both dynamic and static response. Comparison with experimental data indicates that the calibrated model provides a consistent representation of the bridge stiffness and mass distribution. The calibrated model provides a robust baseline for analyzing the bridge behavior under various loading scenarios, verifying serviceability conditions, and interpreting data from the permanent monitoring system, supporting future assessments of the structure health. Acknowledgments This work was carried out within the framework of the PR-FESR project “DIGI-BRIDGE”. The financial support of the Emilia-Romagna Region is gratefully acknowledged. The authors also gratefully acknowledge Ferrovie Emilia Romagna (FER) for the support and the authorizations granted for the bridge monitoring. References Brincker, R., Ventura, C. E., Andersen, P., 2001. Damping estimation by frequency domain decomposition. In Proceedings of IMAC 19: A Conference on Structural Dynamics. Kissimmee, Florida, 698–703. Castagnetti, C., Bassoli, E., Vincenzi, L., Mancini, F. 2019. Dynamic assessment of masonry towers based on terrestrial radar interferometer and accelerometers Sensors (Switzerland) 19(6), 1319. Comanducci, G., Magalhães, F., Ubertini, F., Álvaro Cunha, 2016. On vibration-based damage detection by multivariate statistical techniques: Application to a long-span arch bridge. Structural Health Monitoring 15, 505–524. Dong, C. Z., Bas, S., Catbas, F. N., 2020. Investigation of vibration serviceability of a footbridge using computer vision-based methods. Engineering Structures 224, 111224. Maes, K., Lombaert, G., 2021. Monitoring railway bridge kw51 before, during, and after retrofitting. Journal of Bridge Engineering 26, 04721001. Magalhães, F., Cunha, A., Caetano, E., 2012. Vibration based structural health monitoring of an arch bridge: From automated oma to damage detection. Mechanical Systems and Signal Processing 28, 212–228. Peeters, B., De Roeck, G., 1999. Reference-based stochastic subspace identification for output-only modal analysis. Mechanical Systems and Signal Processing 13(6), 855–878. Poluzzi, L., Barbarella, M., Tavasci, L., Gandolfi, S., Cenni, N. 2019. Monitoring of the Garisenda Tower through GNSS using advanced approaches toward the frame of reference stations, Journal of Cultural Heritage 38, 231-241. Ponsi, F., Bassoli, E., Vincenzi, L., 2021, A multi-objective optimization approach for FE model updating based on a selection criterion of the preferred Pareto-optimal solution. Structures 33, 916–934. Ponsi, F., Bassoli, E., Vincenzi, L., 2023. Mitigation of model error effects in neural network-based structural damage detection. Frontiers in Built Environment 8, 1109995. Ranieri, C., Notarangelo, M. A., Fabbrocino, G., 2020. Experiences of Dynamic Identification and Monitoring of Bridges in Serviceability Conditions and after Hazardous Events. Infrastructures 5(10), 86. Romanazzi, A., Scocciolini, D., Savoia, M., Buratti, N., 2023. Iterative hierarchical clustering algorithm for automated operational modal analysis. Automation in Construction 156. Vincenzi, L., Bassoli, E., Ponsi, F., Castagnetti, C., Mancini, F., 2019, Dynamic monitoring and evaluation of bell ringing effects for the structural assessment of a mansory bell tower. Journal of Civil Structural Monitoring 9, 439–458. Vincenzi, L., Gambarelli, P., 2017, A proper infill sampling strategy for improving the speed performance of a Surrogate-Assisted Evolutionary Algorithm. Computers and Structures 178, 58–70. Zona, A., 2020. Vision-based vibration monitoring of structures and infrastructures: An overview of recent applications. Infrastructures 6(1), 4.