Performance of Pavement Temperature Prediction Models

Abstract

Appropriate asphalt binder selection is dependent on the correct determination of maximum and minimum pavement temperatures. Temperature prediction models have been developed to determine pavement design temperatures. Accordingly, accurate temperature prediction is necessary to ensure the correct design of climate-resilient pavements and for suitable pavement overlay design. Research has shown that the complexity of the model, input variables, geographical location among others affect the accuracy of temperature prediction models. Calibration has also proved to improve the accuracy of the predicted temperature. In this paper, the performance of three pavement temperature prediction models with a sample of materials, including asphalt, was examined. Furthermore, the effect of calibration on model accuracy was evaluated. Temperature data sourced from Pretoria were used to calibrate and test the models. The performance of both the calibrated and uncalibrated models in a different geographical location was also assessed. Asphalt temperature data from two locations in Ghana were used. The determination coefficient (R2), Variance Accounted For (VAF), Maximum Relative Error (MRE) and Root Mean Square Error (RMSE) statistical methods were used in the analysis. It was observed that the models performed better at predicting maximum temperature, while minimum temperature predictions were highly variable. The performance of the models varied for the maximum temperature prediction depending on the material. Calibration improved the accuracy of the models, but test data relevant to each location ought to be used for calibration to be effective. There is also a need for the models to be tested with data sourced from other continents.

Evaluation of Selected Empirical Models for Asphalt Pavement Temperature Prediction in a Tropical Climate: The Case of Ghana

“…This could have affected the prediction accuracy of these models. The R 2 and RMSE values produced for Kumasi by the Diefenderfer [11] model compared well with Lekea and Steyn [27] findings, where an evaluation of the Diefenderfer [11] model in Pretoria yielded R 2 of 0.378 and RMSE of 4.6 °C. However, higher R 2 and RMSE values were recorded by the Diefenderfer [11] model for Tamale than Pretoria [27].…”

“…This could have affected the prediction accuracy of these models. The R 2 and RMSE values produced for Kumasi by the Diefenderfer [11] model compared well with Lekea and Steyn [27] findings, where an evaluation of the Diefenderfer [11] model in Pretoria yielded R 2 of 0.378 and RMSE of 4.6 • C. However, higher R 2 and RMSE values were recorded by the Diefenderfer [11] model for Tamale than Pretoria [27]. As seen in Figure 11, the Taamneh [12] model-predicted pavement tem sus measured pavement temperature is scattered above the LOE, indicati errors between the predicted and measured temperatures.…”

Marshall Asphalt Mix and Superior Performance Asphalt Mix in Oman: A Comparative Study

“…The most frequent bitumen grade utilized in asphalt mixtures in this region is the asphalt binder 60/70 penetration grade for Marshall mixtures [11]. However, the Super-pave Performance Grading (PG) system appropriately takes into account the pavement conditions as it links the measured physical qualities of asphalt binders with field performance [12,13]. Although other elements, such as the geographic location, also play a significant influence, air temperature has the greatest impact on pavement temperature.…”

“…However, the recommendation from [11] included asphalt binder of PG 64-10 for Oman with 95% dependability. The environmental factors affect the engineering properties [9,13,14,16,17]. This paper reports the results of an experimental investigation regarding the performance of asphalt mixtures using asphalt binder with 60/70 penetration grade and PG 64-10.…”