Bridge roughness scanned by Dual-Wheeled 3D test vehicle and processed by augmented Kalman filter: Theory and application

Abstract

A novel method is presented for estimating the bridge surface roughness scanned by a single-axle dual-wheeled 3D test vehicle and processed by an augmented Kalman filter (AKF). Two acceleration sensors are installed atop the axle near the two wheels of the vehicle to measure its vertical and rocking motions. Meanwhile, the Kalman filter algorithm is augmented specially for the vehicle-bridge interaction (VBI) system, allowing the bridge surface roughness to be treated as the only unknown in the state-space formulation. To meet the invertibility criterion for resolving the dynamic VBI problems using the AKF, the observation vector is restructured by consolidating the accelerations recorded for the two wheels and their derivative displacements. The effectiveness of the present method was validated by the finite element method and demonstrated in a parametric study encompassing various system properties. In addition, a self-made, single-axle, dual-wheeled test vehicle was adopted in the field test to verify the theory presented. The reliability of the present technique was confirmed by its application to a real three-span continuous concrete girder bridge. The results indicate that the present technique is suitable for detecting bridge surface roughness of all levels with low sensitivity to noise interference and vehicle damping. Moreover, the surface elevations identified along the traces of the left and right wheels of the moving vehicle are “spatial” in nature. For practical application, it is recommended that the vehicle operates at speeds not exceeding 12 m/s to keep errors below 2 %.