Vehicle axle identification using wavelet analysis of bridge global responses

Abstract

Bridge weigh-in-motion (BWIM) technique uses an instrumented bridge as a weighing scale to estimate vehicle weights. Traditional BWIM systems use axle detectors placed on the road surface to identify vehicle axles. However, the axle detectors have poor durability due to the direct exposure to the traffic. To resolve this issue, a free-of-axle-detector (FAD) algorithm, which eliminates the use of axle detectors, was proposed. As a further improvement to simplify the BWIM systems, the concept of nothing-on-road (NOR) BWIM was recently introduced. The axle identification method proposed in this paper is an attempt to achieve the NOR BWIM, i.e., using bridge global responses to identify vehicle axles. Wavelet analysis is applied to extract the axle information from the global responses. This allows the BWIM technique to be achieved with only weighing sensors. Numerical simulations are conducted using three-dimensional vehicle and bridge models and the effect of several parameters, including sampling frequency, road surface condition and measurement noise on the identification accuracy is investigated. The results demonstrate that the proposed identification method using wavelet analysis can accurately identify vehicle axles, except for cases where the road surface condition is rough or measurement noises exceed certain levels.