Evaluation of Different Regression-Based Models to Predict Depth Temperature of Asphalt Layers Using Field Experiments in Iran

In this study, using the results from field experiments in six asphalt pavement sites located in five provinces with different climatic conditions in Iran, depth temperature of asphalt layers was determined. For this purpose, four well-known regression models including Gedafa et al., Albayati and Alani, BELLS, and Park et al. were utilized. Two statistical criteria, accuracy and bias have been used for evaluating the performance and capability of these models in predicting the depth temperature of asphalt layers. Results showed there is no good correlation between the predicted depth temperature values and those measured during the Falling Weight Deflectometer (FWD) testing. Furthermore, it is necessary to increase the prediction accuracy and decrease its bias by calibrating the mentioned models to use in determining the depth temperature of asphalt layers in local pavements. Among the investigated models, Albayati and Alani model was selected as the best model to predict the depth temperature of asphalt layers with the highest accuracy and the lowest bias (acceptable correlation between predicted and measured values).