Development of statistical temperature prediction models for a test road in Edmonton, Alberta, Canada

Abstract

This paper focuses on developing empirical statistical pavement temperature prediction models based on two years of field data collected from an instrumented test road located in Edmonton, Alberta, Canada, from September 2014 to September 2016. Step-wise regression analysis was utilized for developing the regression models based on the most important predictors. The developed models were divided into four categories, including average daily pavement temperature for cold and warm seasons and maximum and minimum daily pavement temperatures at any depth throughout the asphalt layer. One of the models in the literature was adopted and calibrated for the test road to predict instantly the pavement temperature at different depths, which is highly useful for precisely analyzing the results of Falling Weight Deflectometer (FWD) tests. All the models presented in this study showed a significantly high coefficient of correlation. The models were validated using the field data from September to October 2016 and showed satisfactory results.