Keywords
Abstract
This study presents the results of mathematical modeling of the thermal conductivity and strength properties of asphalt concrete mixtures, utilizing a three-factor Box–Behnken experimental design implemented within the STATISTICA software environment. The variable factors considered in the modeling were the temperature of technological processing, the duration of thermal exposure, and the gravel-to-bitumen content ratio in the mixture. The main objective was to quantitatively evaluate the impact of composition and thermal strengthening conditions on the thermal and mechanical performance of asphalt concrete. These performance indicators are highly important for the effective design, construction, and long-term operation of road structures in diverse climatic regions. To determine the statistical significance of the studied factors, analysis of variance (ANOVA) was conducted. Regression equations for the response functions were developed, and response surface plots as well as main effects profiles were generated. The results demonstrated that the bitumen content and processing temperature significantly influenced the strength characteristics, whereas thermal conductivity exhibited lower sensitivity to parameter variations. High values of determination coefficients (R² and adjusted R²) confirmed the consistency between the statistical models and experimental outcomes. In addition, engineering calculations addressing pavement freezing depth and design soil moisture were performed using the modeled thermal properties of asphalt concrete. These findings confirmed the practical applicability of the developed model, showing that optimization of mixture composition contributes to improved pavement durability, enhanced energy efficiency, and the development of updated regulatory standards and advanced road construction technologies.