Evaluation of pavement surface roughness performance under multi-features conditions based on optimized random forest

Abstract

The international roughness index (IRI) is an important indicator reflecting the roughness of the road surface. Pavement roughness performance is affected by many factors such as pavement surface disease, pavement service life and natural environment. In view of this, this paper aims to study an IRI prediction model based on the random forest algorithm of multi-feature analysis. The mechanism of influencing factors of pavement roughness was analyzed to extract IRI and its influencing factors data in the seven major states of the United States for nearly 26 years from the long term pavement performance (LTPP) database, and then an asphalt pavement roughness dataset was established. Furthermore, through the feature selection based on the linear Pearson correlation coefficient, feature heat map and the nonlinear random forest, XGBoost (extreme Gradient Boosting) algorithm, the factors that affect the road surface roughness were analyzed. Finally, in order to effectively fit the IRI through the pavement surface roughness influencing factors, a random forest model based on grid search optimization was constructed. Optimized random forest was compared with the traditional method KNN, decision tree and random forest to verify their performance on the pavement roughness data set. Experiments show that the random forest prediction model based on grid search optimization has higher prediction accuracy, and the fitting goodness R² is as high as 98.6%.