A mathematical prediction model for calculating the thermal conductivity of the asphalt mixture

This study aims to develop a mathematical prediction model suitable for calculating the thermal conductivity of the asphalt mixture. Theoretically, a parallel-serial-geometric mean model was proposed by correlating the number of phases of the asphalt mixture with the arrangement structure. This model could convert the asphalt mixture from three to two phases without ignoring any phases. Then, the final prediction model was proposed by introducing the influences of the thermal conductivity chain (TCC) and water into the parallel-serial-geometric mean model. Experimentally, compaction times, asphalt-aggregate ratio and gradation were chosen as the experimental parameters for specimen preparation. The dry and wet specimens’ thermal conductivity was measured after the steady-state method’s improvement. Results show that the thermal conductivity increases with the increase of compaction times, asphalt-aggregate ratio and water content, and the water content is linearly related to thermal conductivity, but gradation has almost no influence. The degree of influence of the different factors on the thermal conductivity is ranked as follows: compaction times > asphalt-aggregate ratio > water content > gradation. The voids and TCC must be considered when calculating the thermal conductivity of the asphalt mixture thermal conductivity. Moreover, the influences of parallel and series arrangements on the thermal conductivity are represented by the exponential, which is linearly related to the volume fraction of each component. The prediction model proposed in this article considers the influences of asphalt, aggregates, voids and water. The experimental data validated the high accuracy of the prediction model.