Performance of Pavement Temperature Prediction Models
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 de...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/7/4164 |
| _version_ | 1797608388642734080 |
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| author | Angella Lekea Wynand J. vdM. Steyn |
| author_facet | Angella Lekea Wynand J. vdM. Steyn |
| author_sort | Angella Lekea |
| collection | DOAJ |
| description | 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 (<i>R</i><sup>2</sup>), Variance Accounted For (<i>VAF</i>), Maximum Relative Error (<i>MRE</i>) and Root Mean Square Error (<i>RMSE</i>) 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. |
| first_indexed | 2024-03-11T05:42:48Z |
| format | Article |
| id | doaj.art-55335b7ace614e8eadca15f3dd31694b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T05:42:48Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-55335b7ace614e8eadca15f3dd31694b2023-11-17T16:16:25ZengMDPI AGApplied Sciences2076-34172023-03-01137416410.3390/app13074164Performance of Pavement Temperature Prediction ModelsAngella Lekea0Wynand J. vdM. Steyn1TRL Limited, Workingham RG40 3GA, UKDepartment of Civil Engineering, University of Pretoria, Pretoria 0002, South AfricaAppropriate 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 (<i>R</i><sup>2</sup>), Variance Accounted For (<i>VAF</i>), Maximum Relative Error (<i>MRE</i>) and Root Mean Square Error (<i>RMSE</i>) 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.https://www.mdpi.com/2076-3417/13/7/4164temperature prediction modelsasphalt binderclimate resiliencepavement temperature |
| spellingShingle | Angella Lekea Wynand J. vdM. Steyn Performance of Pavement Temperature Prediction Models Applied Sciences temperature prediction models asphalt binder climate resilience pavement temperature |
| title | Performance of Pavement Temperature Prediction Models |
| title_full | Performance of Pavement Temperature Prediction Models |
| title_fullStr | Performance of Pavement Temperature Prediction Models |
| title_full_unstemmed | Performance of Pavement Temperature Prediction Models |
| title_short | Performance of Pavement Temperature Prediction Models |
| title_sort | performance of pavement temperature prediction models |
| topic | temperature prediction models asphalt binder climate resilience pavement temperature |
| url | https://www.mdpi.com/2076-3417/13/7/4164 |
| work_keys_str_mv | AT angellalekea performanceofpavementtemperaturepredictionmodels AT wynandjvdmsteyn performanceofpavementtemperaturepredictionmodels |