Rutting prediction and analysis of influence factors based on multivariate transfer entropy and graph neural networks.

The Rutting prediction model is an essential element of efficient pavement management systems. Accuracy of commonly used predictive model necessitates knowledge of the input parameters that was incorporated and local calibration of the model coefficients. In this paper, a novel rutting prediction model based on multivariate transfer entropy and graph neural networks is proposed for incorporating a limited number of observable inputs, which can accommodate with sufficient prediction performance and generalization to a variety of complex pavement design structure data. The multivariate transfer entropy based graph representation is able to find the significant causality between variables and rutting. The influence factor analysis results confirm the high influence of temperature and vehicle axle load. Several experiments are set up on the Research Institute of Highway Ministry of Transport track (RIOHTrack) dataset for the comparison between the proposed model and the state-of-art prediction models. The result demonstrates that the proposed model is more accurate and robust compared to existing methods on the rutting prediction task.