The moving load identification method on asphalt roads based on the BP neural network and FBG sensor monitoring

Abstract

This study to accurately identify the load information of vehicles on asphalt roads is based on the measured data of the fiber Bragg grating (FBG) sensor and the Back Propagation (BP) neural network. First, the input parameters were determined by the BP neural network and FBG sensor theory. Second, the internal mechanical response of asphalt roads under different working conditions was calculated by using finite element software and by using the results of numerical simulation trained in the neural network. Third, the accuracy of the BP neural network used to identify the moving load on asphalt roads was verified by the field-measured data of the FBG sensor. Fourth, the influential factors of moving load identification accuracy were analyzed. The main conclusions are as follows: (a) the sensitive factors of moving load identification on asphalt roads include vehicle speed, asphalt layer temperature, the longitudinal strain between asphalt layers, the number of wheels, and load position; (b) the average absolute error of moving load identification is about 4.11kN, and the average recognition accuracy is up to 90.57%; (c) the measurement accuracy is sensitive to the speed of the vehicle. When vehicle speed exceeds 60 km/h, recognition accuracy may decrease to less than 90%; and (d) the loads at different locations have a significant influence on moving load identification.