Using Kalman filter to estimate the pavement profile of a bridge from a passing vehicle considering their interaction

Pavement irregularity is an unknown source that affects the riding comfort and controllability of a moving vehicle. In the vehicle scanning method, the frequency-domain information of pavement irregularity is crucial to the effective extraction of the bridge's parameters from the vehicle's dynamic responses. To this end, the vehicle-bridge interaction (VBI) system considering pavement irregularity is first established in the state-space model. Upon constructing the measurement vector, the discrete Kalman filter with unknown input algorithm is introduced to estimate the state of the VBI system and pavement irregularity. The feasibility of the proposed method is verified by comparing the estimated results with the original assumed ones. The parametric study concerning the effects of the VBI, vehicle's velocity, measurement noise, and damping effects on the estimated results further demonstrates the effectiveness of the proposed method.

Automated summary

This study establishes a vehicle-bridge interaction system considering pavement irregularity and utilizes a discrete Kalman filter to estimate the system state and pavement irregularity. The method's effectiveness is verified through a parametric study on the effects of various factors on the estimated results.