This paper presents a new moving force identification (MFI) algorithm that uses measured accelerations to infer applied vehicle forces on bridges. Previous MFI algorithms use strain or deflection measurements. Statistics of the inferred forces are used in turn as indicators of global bridge damage. The new acceleration-based MFI algorithm (A-MFI) is validated through numerical simulations with a coupled vehicle-bridge dynamic interaction model programmed in MATLAB. A focussed sensitivity study suggests that results are sensitive to the accuracy of the vehicle velocity data. The inferred Gross Vehicle Weight (GVW), calculated by A-MFI, is proposed as the bridge damage indicator. A real weigh-in-motion database is used with a simulation of vehicle/bridge interaction, to validate the concept. Results show that the standard deviation of inferred GVWs has a good correlation with the global bridge damage level.

This paper proposes a new indirect bridge structural health monitoring concept that uses acceleration data from a fleet of different vehicles with unknown weights. When a vehicle passes the bridge, the vertical displacement under its axles can be inferred from its vertical accelerations. This displacement, termed the “apparent profile”, contains two components: bridge profile elevations and bridge deflections under the axle. The two deflection component can be used to find the moving reference influence function (MRIF), defined as the deflection at a (moving) reference point due to a unit load at another point, moving at the same speed. The MRIF can be found when all axle weights are known. In this paper, a new method is proposed to obtain road profile and bridge health condition from the vehicle acceleration, without knowing individual axle weights. Numerical simulation results show that the inferred bridge profile changes when the bridge health condition changes. The difference can be used as an indicator of bridge damage and is illustrated here through an example of bearing damage.