Summary This paper presents a novel damage identification technique for bridges based on the dynamic response of a moving vehicle. A quarter car vehicle model instrumented with two accelerometers and an inertial profilometer is used for this purpose. The method involves the coupling of Tikhonov regularization scheme with signal averaging technique to handle the problem of measurement noise and road roughness. In the first stage, a damage‐dependent road roughness profile is estimated from measured acceleration response by minimizing a Tikhonov regularized least squares cost function. The second stage involves the minimization of the profile roughness residual function that depends on the location and magnitude of damage. This objective function compares the roughness profile computed from the first stage and that measured by the inertial profilometer. It is proved that efficient damage detection ensues from considering multiple runs of the vehicle and choosing the appropriate regularization parameter. The present study considers various aspects, such as the uniqueness of results, robustness of results to measurement noise, effect of modelling error, effect of vehicle speed, and uncertainty in estimation. Numerical results show that the approach is capable of detecting the magnitude as well as the location of damage. In addition, the problem of road roughness and measurement noise is handled competently.

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This paper deals with an indirect health monitoring strategy for bridges using an instrumented vehicle. Thermodynamic principles are used to relate the change in Vehicle–Bridge-Interaction (VBI) forces to the change in dynamic tyre pressure. The damage identification process involves two stages. In the first stage, the unknown tyre model parameters are estimated using Bayesian inference based on the calibration data. The approach uses a Stein variational gradient descent implementation of the Bayes rule to quantify the uncertainty in the estimated tyre parameters. In the second stage, the calibrated tyre model is used to reconstruct the change in VBI force from measured tyre pressure data considering a damaged bridge. It is observed that damage present in the bridge produces notable changes in VBI force. Contour plots based on VBI force and natural frequency are developed for damage detection. The reconstructed VBI force change is used to quantify damage using the contour plots. Further, the least square estimation approach is adopted for damage identification by defining appropriate objective functions and imposing constraints on the damage indicators. The damage is estimated by minimizing the objective function using Cuckoo search algorithm. Numerical experiments reveal that the developed method could be used for accurate damage identification in the presence of measurement noise, uncertainty in estimated tyre parameters, and the uncertainty in bridge model parameters.

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