Damage identification and model updating using bridge displacement under moving vehicles with uncertain weight

Bridge damage incurred during long-term service leads to discrepancies between the physical structure and its digital twin finite element (FE) model, necessitating regular model updates to ensure reliable health monitoring. Traditional methods typically adopt a two-step framework: first estimating the traffic loads, and then performing damage identification and model updating based on the bridge responses under these known loads. However, inevitable errors in load estimation propagate into the structural analysis, leading to inaccurate damage assessments and biased updated models. To address this issue, this paper proposes a novel method for bridge damage identification and model updating that eliminates the reliance on precise load information. By developing load-independent indices through normalization and ratio operations, the structural stiffness characteristics are mathematically decoupled from unknown vehicle weights. Specifically, a normalized curvature method is first employed to locate damaged regions using displacement data, and a displacement ratio method is then used to quantify the damage severity. Subsequently, a local gradient descent approach is adopted to inversely update the stiffness parameters of the identified damaged regions in the FE model. Both numerical simulations on a simply supported beam and experiments on a scaled bridge model were conducted to verify the effectiveness and robustness of the proposed method. The results demonstrate that the method achieves accurate damage identification and robust model updating across various damage cases. The modal frequency errors of the updated models are consistently less than 1%, and the root mean square error (RMSE) of the displacement time-history response at each measurement point remains below 0.3 mm. By removing the need for exact load information, this study provides a reliable framework for maintaining bridge digital twin models.