Abstract
A statistical model for predicting pavement surface temperature has been developed for the first time in this study. The main objective of this work is to analyse the impact of the input factors (solar radiation, ambient temperature, wind speed, and albedo) most affecting the conventional rigid pavement surface temperature. A multi-physics numerical model combining radiative, conductive, and convective heat transfers with a response surface methodology (RSM) model was created as an innovative and integrated approach. The numerical model of two-dimensional heat transport based on volume finite techniques is performed using FORTRAN software. Moreover, the analysis of variance (ANOVA) is used to establish the polynomial model or new correlation (quadratic regression) and analyse each input factor's statistical importance. Heat transfer model findings were in good agreement with the experimental data. For pavement surface temperature, the results show that the adjusted R-adj(2) and predicted determination coefficients R-2 achieved are 0.9912 and 0.9604, respectively. A good agreement is obtained between the statistical model's prediction values and the heat transfer model's numerical data. Based on the four analysed input factors, a simple polynomial statistical model is developed to forecast and reduce the conventional rigid surface pavement temperature. A wind speed of 1.215 m/s, an air temperature of 293.634 K, solar radiation of 222.305 W/m2 and albedo of 0.782 were found to be the optimal operating conditions for obtaining the lowest possible temperature on the pavement's surface.