Experimental analysis of vehicle–bridge interaction using a wireless monitoring system and a two-stage system identification technique

Deterioration of bridges under repeated traffic loading has called attention to the need for improvements in the understanding of vehicle–bridge interaction. While analytical and numerical models have been previously explored to describe the interaction that exists between a sprung mass (i.e., a moving vehicle) and an elastic beam (i.e., bridge), comparatively less research has been focused on the experimental observation of vehicle–bridge interaction. A wireless monitoring system with wireless sensors installed on both the bridge and moving vehicle is proposed to record the dynamic interaction between the bridge and vehicle. Time-synchronized vehicle–bridge response data is used within a two-stage system identification methodology. In the first stage, the free-vibration response of the bridge is used to identify the dynamic characteristics of the bridge. In the second stage, the vehicle–bridge response data is used to identify the time varying load imposed on the bridge from the vehicle. To test the proposed monitoring and system identification strategy, the 180 m long Yeondae Bridge (Icheon, Korea) was selected. A dense network of wireless sensors was installed on the bridge while wireless sensors were installed on a multi-axle truck. The truck was driven across the bridge at constant velocity with bridge and vehicle responses measured. Excellent agreement between the measured Yeondae Bridge response and that predicted by an estimated vehicle–bridge interaction model validates the proposed strategy.

► Wireless monitoring system proposed with sensors installed both the bridge and moving vehicles. ► Two-stage system identification used to predict bridge response to a truck loading profile. ► The proposed wireless monitoring system is shown to be highly reliable and accurate. ► Wireless monitoring system validated on the 180 m long Yeondae Bridge (Icheon, Korea). ► Excellent agreement observed between measured data and vehicle–bridge interaction model.