This paper describes a novel method to determine a road profile through the analysis of accelerations in a passing vehicle. A direct integration algorithm is proposed to determine the profile from the measured vehicle acceleration response. A sprung mass model and a half-car model are used to represent the vehicles in separate analyses. Combining the direct integration algorithm with the Cross Entropy (CE) optimisation method, a vehicle fleet monitoring concept is proposed for the monitoring of roads and/or bridges. In this approach, the profile can be calculated using accelerations from multiple vehicles without prior knowledge of the vehicle properties. Numerical results show that calculated profiles are the same as the ‘true’ profiles which were used to generate the ‘simulated measured’ accelerations.

This study introduces a novel method to determine apparent profile of the track and detect railway bridge condition using sensors on in-service trains. The concept uses a type of Inverse Newmark-β integration scheme on data from a batch of trains. In a self-calibration process, an optimization algorithm is used to find vehicle dynamic properties and speed. For bridge health monitoring, the apparent profile of the bridge is first determined, i.e., the true profile plus components of ballast and bridge deflection under the moving train. The apparent profile is used, in turn, to calculate the moving reference deflection influence line, i.e., the deflection due to a moving (static) unit load. The moving reference influence line is shown to be a good indicator of bridge stiffness. This numerical approach is assessed using an elaborate finite element model operated by an independent research group. The results show that the moving reference influence line can be found accurately and that it constitutes an effective indicator of the condition of a bridge.

Bridges are critical components of transport networks, yet they inevitably deteriorate over time. Damage in a bridge reduces its stiffness, altering both its static and dynamic responses to loading. This change also affects the dynamic excitation of the loading source, such as a passing vehicle, meaning that vehicle vibrations can be used to infer bridge damage. Deflections under a moving instrumented axle are influenced by both bridge and vehicle properties, making them unique to each vehicle. To extract meaningful information about bridge conditions, these data must therefore be transformed into a form independent of vehicle characteristics. The bridge influence line, defined as the response to a unit moving load, provides a reliable descriptor of structural behaviour that is unaffected by vehicle properties. In this study, a fleet of vehicles is simulated to generate acceleration signals, from which bridge influence lines are derived using the inverse Newmark–beta method. Two damage indicators are proposed: D1, derived from the mid-span point of the mean Moving Reference Influence Line (MRIL), and D2, defined as the area under the mean MRIL curve. Results show that both indicators can effectively detect global bridge damage. D2 exhibits a clear linear relationship with stiffness loss and remains stable under varying speeds and noise levels, whereas D1 is more sensitive to factors such as measurement noise, vehicle speed, and variations in vehicle properties, but remains reliable at lower speeds and moderate noise levels. Measurement errors are simulated by adding random noise to the acceleration inputs, and the fleet monitoring approach is shown to mitigate these effects, enhancing the overall accuracy and robustness of bridge damage detection.