A novel approach for asphalt pavement invisible distress classification prediction by machine learning algorithms

Abstract

ABSTRACT The detection and identification of road invisible distress (ID) has always been a key issue of concern for pavement maintenance and rehabilitation (M&R) optimisation, especially for asphalt pavement with semi-rigid base. This study is to build a supervised machine learning model based on the detected characteristic variable data of the pavement structure strength, roughness, cracking, permeability, traffic volume, etc. It is to fit the model with class labelled data on the training set and to train an algorithm model that can intelligently detect the degree of ID. In addition, K-fold cross-validation and hyperparameter search are employed to find the optimal machine learning suitable for road ID category prediction. The results show that Gradient Boosting machine (GBM) model, LightGBM model and Random Forest (RF) model are marketed out from the other six models, for their classification performance of the models is the best, with statistical indicators up to 96%. Additionally, the LightGBM model achieves the best results with shortest computation time and 99% the accuracy of the test set. Furthermore, it also indicates that the three main factors affecting the ID are permeability, alligator cracking and equivalent single axle load (ESAL).