Fleet Monitoring of Bridge Using Direct Calculation of Moving Reference Influence Line

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.