Sensor-Informed Finite Element Model Updating of a Railway Bridge Using Hybrid Differential Evolution–Levenberg–Marquardt Optimisation: A Case Study

Field displacement measurements can support FE model updating, although the resulting model remains conditional on the available observations and modeling assumptions. This study proposes a hybrid Differential Evolution–Levenberg–Marquardt (DE–LM) framework for updating an FE model using train-induced displacement histories measured by linear variable differential transformers (LVDTs). A prestressed-concrete box-girder railway bridge was investigated, with the numerical model explicitly representing the box girder, sleepers, rails, and equivalent rail-fastening connections. Preliminary parameter screening was conducted to select three influential parameters: the box-girder elastic modulus and the effective vertical and lateral fastening stiffnesses. DE was employed to explore the bounded parameter space, after which LM refined the best global solution using local response sensitivities. Physical parameter bounds and regularisation were incorporated to limit non-unique and unrealistic solutions. Complete train passages were separated into calibration and independent validation datasets. The hybrid procedure reduced the initial objective value by approximately 90.5% and produced physically admissible parameter changes. For the independent validation dataset, the updated model achieved a mean normalized root mean square error of 2.17%, compared with 3.07% for DE-only, 4.94% for LM-only, and 7.46% for the initial model. The mean peak-displacement error was reduced to 0.70%. For the investigated case, the selected hybrid solution achieved lower validation errors than the reported DE-only and nominal-start LM solutions. The calibrated model provides a physics-based foundation for subsequent railway-bridge digital-twin development.