Academy & Industry Research Collaboration Center (AIRCC)

Volume 10, Number 13, October 2020

Data Prediction of Deflection Basin Evolution of Asphalt Pavement Structure
Based on Multi-Level Neural Network


Shaosheng Xu, Jinde Cao and Xiangnan Liu, Southeast University, China


Aiming at reducing the high cost of test data collection of deflection basins in the structural design of asphalt pavement and shortening the long test time of new structures, this paper innovatively designs a structure coding network based on traditional neural networks to map the pavement structure to an abstract space. Therefore, the generalization ability of the neural network structure is improved, and a new multi-level neural network model is formed to predict the evolution data of the deflection basin of the untested structure. By testing the experimental data of RIOHTRACK, the network structure predicts the deflection basin data of untested pavement structure, of which the average prediction error is less than 5%.


multi-level neural network, Encoding converter, structural of asphalt pavement, deflection basins, RIOHTRACK.