Embedding Model-form Uncertainty in Probabilistic Calibration of Digital Twins for Bridges
Digital twins of bridges rely on physics-based models to infer full-field structural responses from sparse monitoring data. The reliability of these predictions depends on the calibration of model parameters while accounting for discrepancies between the model and the physical system.