Development of a temperature prediction model for asphalt pavements considering air temperature data of preceding hours

• Published in: The international journal of pavement engineering 2023-01, Vol.24 (2)
• Main Authors: Walia, Ashish, Rastogi, Rajat, Kumar, Praveen, Jain, S. S.
• Format: Article
• Language: English
• Subjects: Air temperature; Asphalt pavement; Asphalt pavements; Developing countries; effective depth; instrumented track; LDCs; pavement temperature; Prediction models; Predictions; Relative humidity; Temperature; temperature prediction model; Weather stations; Wind speed
• ISSN: 1029-8436; 1477-268X
• DOI: 10.1080/10298436.2022.2132245

The structural performance of an asphalt pavement is substantially affected by its temperature state. The in-depth temperature is usually measured through drilling a hole in the pavement. As an alternative, researchers have proposed temperature prediction models considering attributes like air temperature, depth, time, location, solar intensity, wind speed, and relative humidity. But procuring data on many attributes is tedious for the field professionals and at times is not available locally in developing countries. It was observed that most of the existing models predict maximum and minimum temperatures, whereas few models predict the temperature at a fixed depth. This paper presents a pavement temperature prediction model which uses air temperature of preceding hours, time in a day, and depth of the measurement. These attributes are easily available in developing countries. Data were collected through an instrumented track and from a weather station. Contrary to the use of 1-5-day air temperature, it was found that the average air temperature of the preceding few hours is sufficient in predicting the pavement temperature. The prediction accuracy and validation results of the model were found good as compared to some prevailing models.