PSI - Issue 84

Andrea Mileto et al. / Procedia Structural Integrity 84 (2026) 829–836

836

maximum and minimum acceleration values, significantly improves the identifiability of load parameters by reducing these equivalence regions. This result confirms the importance of a careful and physically informed definition of the objective function in inverse dynamic problems. The analysis of modal truncation effects has further clarified the limits and potential of reduced-order modeling. While a truncated modal basis can be effectively employed for straight bridges without compromising identification accuracy, the same strategy proves critical for skew bridges, where flexural–torsional coupling requires a sufficiently rich modal representation to preserve essential dynamic information. These findings provide practical guidance for selecting model complexity in relation to bridge geometry and desired computational efficiency. Overall, the proposed methodology proves to be robust and versatile, offering a promising tool for vehicle load identification using accelerometer data from existing monitoring systems. The results support its potential applicability to real-life scenarios and digital twin implementations, paving the way for future developments involving experimental validation and extension to more complex traffic and bridge configurations. Acknowledgements The Authors kindly acknowledge the Research Project “AID-STRU AgeIng and Degradation in the performances of STRUctures: model- and data-driven tools embedded in digital twins” funded by the Italian Ministerial grant PRIN 2022 n. 2022X9TETW. References Chopra, A., 2012. Dynamics of structures: Theory and applications to earthquake engineering . Pearson Education, Harlow, UK. Di Re, P., Ciambella, J., Lofrano, E., Paolone, A., 2024. Dynamic testing and modeling of span interaction in high-speed railway girder bridges. Measurement 226, 114078. Frýba, L., 1999. Vibration of solids and structures under moving loads . 3rd ed., Thomas Telford Publishing, London, UK. Gattulli, V., Lofrano, E., Paolone, A., Potenza, F., 2019. Measured properties of structural damping in railway bridges. Journal of Civil Structural Health Monitoring 9, 639–653. Ghazal, H., Mwafy, A., 2023. Comparative performance evaluation of retrofit alternatives for upgrading simply supported bridges using 3D fiber based analysis. Buildings 13(5), 1161. Green, M., Cebon, D., 1997. Dynamic interaction between heavy vehicles and highway bridges. Computers & Structures 62(2), 253–264. Han, Z., Kim, C.-W., Chang, K.-C., 2024. A framework specialized for large-scale vehicle–bridge interaction simulation. Computers & Structures 301, 107429. Law, S.S., Zhu, X.Q., 2004. Vehicle–bridge interaction dynamics . World Scientific, Singapore. Lofrano, E., Paolone, A., Vasta, M., 2016. Identification of uncertain vibrating beams through a perturbation approach. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering 2(2), C4015006. Mileto, A., Arena, A., Lofrano, E., 2026. Vehicle Loads Identification in Beam Bridges via a Genetic Algorithm. Engineering Structures (under review). Mosleh, A., Jara, J., Varum, H., 2015. A methodology for determining the seismic vulnerability of old concrete highway bridges by using fragility curves. Journal of Structural Engineering and Geo-Technics 5(1), 1–7. Storn, R., Price, K., 1997. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization 11(4), 341. Yang, Y.B., Yau, J.D., Wu, Y.S., 2004. Vehicle–bridge interaction dynamics . World Scientific, Singapore. Yu, Y., Cai, C., Deng, L., 2016. State-of-the-art review on bridge weigh-in-motion technology. Advances in Structural Engineering 19(9), 1514– 1530.