Feasibility study on pavement rutting evaluation method based on smartphone

Abstract

With the continuous increase of constructions in highway, road maintenance has become more and more important. Thus, it is of great significance to develop the rapid, intelligent and real-time detection technologies for road surface conditions. This paper used the self-developed driving data acquisitionAPP to collect the vibration acceleration data during driving, and carried out the feasibility study on the evaluation method of pavement rutting using smartphones. Firstly, the collected vibration acceleration data are de-noised, and the vibration characteristics under different working conditions are analyzed. Secondly, seven time-domain vibration acceleration indexes with high correlation with pavement rutting are extracted, and the dimensions of seven primary indexes are reduced to two independent principal components by principal component analysis. Finally, the rutting evaluation model based on convolutional neural network is established and compared with the results of back propagation neural network and multilayer perceptron neural network. The results show that the average relative error of the rutting evaluation model based on the convolutional neural network is 16.6%, which is lower than the other twomodels. It indicates that the pavement rutting can be evaluated satisfactorily by smartphones. In addition, this paper divided the evaluation results of rutting into four grades (Excellent, Good, Medium and Poor) and displayed them in different colors on the map. This study is of great significance to improve the level of intelligent detection of road rutting and road maintenance management.