Vehicle Axle Load Sensing Based on Distributed Vibration Fiber Technology

Abstract:

This paper presents a method for sensing vehicle axle loads using distributed vibration fiber sensors to detect pavement vibrations. A mapping relationship between fiber optic signals and pavement vibration displacements is established， and analytical models for vehicle axle load inversion are proposed. Full-scale experiments are conducted to validate the accuracy of the proposed vehicle axle load inversion analytical models， while natural vehicle weighing tests are performed to verify the effectiveness of the vehicle axle load perception algorithm. The results demonstrate that the vehicle axle load sensing system， based on the inversion analytical models， achieves a maximum estimation error of 0.98% for individual axle loads and a total weight prediction accuracy of 0.34%. These accuracies meet the national standards of 2% for single axle loads and 5% for total weight. Furthermore， the system accuracy remains unaffected by variations in vehicle speed.