As the main cause of asphalt pavement distress, rutting severely affects pavement safety. Establishing an accurate rutting prediction model is crucial for asphalt pavement maintenance, pavement structure design, and pavement repair. This study explores five machine learning methods, namely Support Vector Regression (SVR), Artificial Neural Network (ANN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Extra Trees, to predict the development of rutting depth using data from RIOHTRack. The model’s performance is measured by comparing the performance evaluation indicators of different models, such as the coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error. The results demonstrate that integrated learning techniques such as RF, GBDT, and Extra Trees works best with R² = 0.9761, 0.9833, and 0.9747. Moreover, the GBFT model can capture the trend of the measured rutting progression curve better than the mechanistic-empirical (M-E) model. The analysis of feature importance reveals that, in addition to external factors such as temperature and axle load, the aggregate of the asphalt concrete layer and air void crucially affect rutting. The higher the base strength, the smaller the rutting depth. The proposed model is highly straightforward and serves as an accessible analysis tool for engineers in practice.

The evaluation of asphalt pavement structures has been a critical challenge in the field due to the practical limitations in methodology. In this paper, we propose a data-driven framework to evaluate structural performance of nineteen widely used asphalt structures in the Research Institute of Highway Ministry of Transport track (RIOHTrack). Specifically, we utilize the unsupervised machine learning method to delineate the similar and disparate performance among tested structures based on four years of falling weight deflectometer (FWD) experiments. Next, the structural performance is investigated on the temporal scale and the dynamic performance variations are captured over the course of the testing. Finally, experimental results are discussed and we provide essential evidence to aid future asphalt pavement design and construction.

In order to evaluate the feasibility of using hard-grade asphalt concrete in China’s highway construction, based on the accelerated loading test of the full-scale track of the Research Institute of Highway Ministry of Transport (RIOHTrack), the long-term service performance of hard-grade asphalt concrete base pavement was studied, and its actual service effect was evaluated. The study found that hard-grade asphalt concrete has higher strength, modulus and high-temperature stability. Although the low-temperature performance is generally not as good as that of asphalt, with a penetration grade (PG) of 60/80, it has equivalent or better cracking resistance in the area where the RIOHTrack is located. During the five-year observation period, the rutting deformation of the pavement structure showed an annual fluctuation increase. In comparison, the rutting resistance of a hard-grade asphalt concrete base structure can be increased by approximately 16%, and the deflection value is smaller, so the long-term performance of the anti-rutting effect and structural bearing capacity are better. The study shows that hard-grade asphalt concrete base pavement is suitable for highway construction in parts of China, where the climate is similar to that of the RIOHTrack location, or the temperature is higher or the latitude is lower.

In order to improve the accuracy and reliability of an existing rutting performance prediction model, based on the long-term observation data of the RIOHTrack’s full-scale pavement structure, the rutting performance prediction model in China’s Specifications for Design of Highway Asphalt Pavement was evaluated, and the model correction method was proposed, which improves the model’s reliability and makes it more suitable for rutting estimation in the region. The research found that the rutting model in China’s Specifications for Design of Highway Asphalt Pavement has significant structural dependence. The model with the highest prediction accuracy and the smallest error is the semi-rigid base asphalt pavement structure with an asphalt concrete layer thickness of 12 cm; the prediction accuracy of other structures is not high. In order to improve the accuracy and reliability of the rutting prediction model, a new model is established by introducing local correction coefficients into the existing model. After local correction, the accuracy of the rutting prediction models for all structures has been greatly improved, and the determination coefficient R² is greater than 0.87. Since the basic data has already reflected the characteristics of different pavement structures and materials, as well as the impact of local climate environment and traffic load conditions, the new model is more suitable for rutting prediction of various pavement structures in the region where the RIOHTrack is located.