Abstract
Temperature variation significantly influences the selection of the asphalt binder grade. Also, since that the asphalt is a viscoelastic material, the critical responses such as surface deflection and stiffness of the asphalt layer will be highly affected by any temperature change. Most of the methods employed by transportation agencies to measure pavement temperature at present involve drilling a bore hole; therefore, generating a hazard that may cause pavement deterioration. A simple and non-destructive method to predict the pavement temperature is one of the important requirements for pavement engineers. This study created a model that can predict the pavement temperature at various depths utilising basic climatic parameters such as air temperature, pavement surface temperature, relative humidity, and precipitation. Required data were extracted from an open source online database and the two generated models for the state of Minnesota and the state of Arizona were reliable with an acceptable coefficient of determination value.
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