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The temperature estimation within asphalt concrete (AC) overlaid on cement-stabilized bases (CSB) is necessary for pavement analysis and design. However, the impact of different CSB gradations and rubberized CSB on AC temperature has not been thoroughly investigated. This study aims to clarify this effect by examining two types of CSB with nominal particle aggregate sizes of 25 mm and 31.5 mm, as well as the substitution of 5%, 10%, and 20% graded aggregates with rubber aggregates (RA) in CSB Dmax 25 using Ansys-based numerical simulations. The modelling also investigated 11 scenarios with different AC thicknesses (hAC) ranging from 6 to 26 cm. The results indicated that CSB Dmax 31.5 reduced the daily maximum temperature fluctuation at the bottom of the AC (∆TbottomAC) by approximately 8% compared to CSB Dmax 25. The inclusion of 5% RA in CSB Dmax 25 decreased ∆TbottomAC by up to 20%. Additionally, the rubberized CSB increased the maximum temperature gradient between the top and bottom of the AC (ΔTmaxAC) by 9.5% with 5% RA and a 6 cm AC thickness; however, this increase was insignificant when hAC exceeded 12 cm. This study also proposed the use of artificial neural network (ANN) models to predict the AC's temperature distribution based on depth, the time of day, surface paving temperatures, and hAC. The proposed ANN model demonstrated high accuracy (R2 = 0.996 and MSE = 0.000685),which was confirmed by the numerical simulations, with an acceptable RMSE ranging from 0.28 °C to 0.67 °C. [ABSTRACT FROM AUTHOR]

The temperature estimation within asphalt concrete (AC) overlaid on cement-stabilized bases (CSB) is necessary for pavement analysis and design. However, the impact of different CSB gradations and rubberized CSB on AC temperature has not been thoroughly investigated. This study aims to clarify this effect by examining two types of CSB with nominal particle aggregate sizes of 25 mm and 31.5 mm, as well as the substitution of 5%, 10%, and 20% graded aggregates with rubber aggregates (RA) in CSB Dmax 25 using Ansys-based numerical simulations. The modelling also investigated 11 scenarios with different AC thicknesses (hAC) ranging from 6 to 26 cm. The results indicated that CSB Dmax 31.5 reduced the daily maximum temperature fluctuation at the bottom of the AC (∆TbottomAC) by approximately 8% compared to CSB Dmax 25. The inclusion of 5% RA in CSB Dmax 25 decreased ∆TbottomAC by up to 20%. Additionally, the rubberized CSB increased the maximum temperature gradient between the top and bottom of the AC (ΔTmaxAC) by 9.5% with 5% RA and a 6 cm AC thickness; however, this increase was insignificant when hAC exceeded 12 cm. This study also proposed the use of artificial neural network (ANN) models to predict the AC’s temperature distribution based on depth, the time of day, surface paving temperatures, and hAC. The proposed ANN model demonstrated high accuracy (R2 = 0.996 and MSE = 0.000685),which was confirmed by the numerical simulations, with an acceptable RMSE ranging from 0.28 °C to 0.67 °C.