Prediction of Skid Resistance in Asphalt Pavements Under High-Temperature and High-Humidity Conditions Using Machine Learning

Öz

The skid resistance of pavement surfaces is a critical factor in ensuring the safety of airport runways and highways. Traditional skid resistance testing methods are inefficient and susceptible to operational interference, rendering them unsuitable for rapid assessment. This study employs laser scanning technology to capture three-dimensional texture data from asphalt mixture specimens and extracts multi-source features, including texture parameters, fractal dimensions, ambient temperature, and relative humidity. Four machine learning models—Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Categorical Gradient Boosting (CatBoost)—were employed to predict the British Pendulum Number (BPN) under high-temperature and high-humidity conditions. The SHAP (SHapley Additive exPlanations) method was used to analyze the contribution of each feature. Results demonstrated that the CatBoost model achieved the highest predictive performance, with a coefficient of determination (R²) of 0.938, a mean absolute percentage error (MAPE) of 2.807%, and a root mean square error (RMSE) of 2.72. Humidity conditions were found to contribute significantly more to model predictions than temperature. Among texture-related features, the three-dimensional fractal dimension (DBC3) played a prominent role in macro-texture models, while the linear profile fractal dimension (DBC1) contributed most to micro-texture models. This research provides an effective approach for intelligent prediction of skid resistance in asphalt pavements under hot and humid climatic conditions.

Anahtar Kelimeler

• Asphalt pavement
• skid resistance
• high temperature and high humidity
• machine learning

Destekleyen Kurum

The Transportation Science and Technology Project of Shandong Province

Proje Numarası

No. 2021B95

Etik Beyan

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Teşekkür

The work is supported by the Transportation Science and Technology Project of Shandong Province (No. 2021B95), The authors are very grateful for the financial contribution and convey their appreciation for supporting this basic research.

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