Ensemble-Based Material-Specific Prediction of Thermal Conductivity for Steel Slag Asphalt Mixtures

Thermal conductivity is a crucial parameter for heat transfer in asphalt pavements, especially in cold regions where electrically heated snow-melting systems are used. Steel slag, an industrial by-product with high thermal conductivity, holds significant potential to enhance the thermal performance of asphalt mixtures. However, its thermal behavior is influenced by various factors. This study established a thermal conductivity database consisting of 200 samples from published experimental studies, incorporating data collection, graphical digitization, and physically constrained expansion. Mixture composition, volumetric structure, and steel slag properties were used as input variables, with thermal conductivity as the output. Five machine learning models including k-nearest neighbors regression, decision tree, random forest, support vector regression, and gradient boosting were developed. Among them, random forest and gradient boosting showed the highest accuracy and robustness. Feature importance analysis revealed that steel slag content is the primary factor affecting thermal conductivity, while material properties and gradation parameters play secondary roles. This data-driven framework facilitates the efficient prediction and design of thermal conductivity in steel slag asphalt mixtures, supporting the engineering application of functional asphalt pavements.