Improved empirical convection heat transfer coefficient model to predict flexible pavement layer temperatures

Abstract

Temperatures in asphalt layers of a flexible pavement significantly influence its performance, and its accurate prediction is vital for effective life-cycle design that models asphalt as a viscoelastic material. In 2023, Rajapaksha et al. observed that convective heat transfer plays a significant role in temperature prediction, and this study focused on developing a more accurate model for Convection Heat Transfer Coefficient (CHTC) to achieve that using a five-step approach. The cyclical nature of temperatures measured at 5-minute time steps for one year prompted a model based on Fourier analysis. Step 1 conducted a parametric study of CHTC that compared predicted and measured temperatures to backcalculate an optimum value for each time step. Step 2 used these values as the basis to develop monthly Fourier models, but they were not sufficiently accurate. When key weather parameters were added to it in Step 3, the resulting monthly empirical models developed using stepwise regression analysis improved to an acceptable level. In Step 4, the monthly CHTC regression models were used to predict pavement temperatures for the whole year that were compared with measured values from a field test section. An error analysis performed in Step 5 revealed that the RMSE of prediction error for the year was 2.178 °C which was very close to the benchmark set at the beginning of this work. In addition, the median error was estimated at −0.09 °C showing that the proposed CHTC model is sufficiently accurate to be used in pavement performance prediction.