Calibration of regression-based models for prediction of temperature profile of asphalt layers using LTPP data
For analysis, design, and rehabilitation purposes of flexible pavements, the temperature profile of asphalt layers should be determined. The predictive models as an alternative to in-situ measurements, are rapid and easy methods to determine the temperature of asphalt layer at various depths. These...
| Những tác giả chính: | , , |
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| Định dạng: | Bài viết |
| Ngôn ngữ: | English |
| Được phát hành: |
Vilnius Gediminas Technical University
2023-03-01
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| Loạt: | Journal of Civil Engineering and Management |
| Những chủ đề: | |
| Truy cập trực tuyến: | https://jbem.vgtu.lt/index.php/JCEM/article/view/18611 |
| Tóm tắt: | For analysis, design, and rehabilitation purposes of flexible pavements, the temperature profile of asphalt layers should be determined. The predictive models as an alternative to in-situ measurements, are rapid and easy methods to determine the temperature of asphalt layer at various depths. These models are developed based on limited field data. Hence, there is a need for developing new models for prediction of temperature profile of asphalt layers in various climatic regions. In this study, climatic data was retrieved from the Long-Term Pavement Performance (LTPP) database. The information of 33 asphalt pavement test sections in 16 states in the United States was employed for calibrating the predictive models. Using the prepared data, the temperature profile of asphalt layers was predicted utilizing four regression-based models, including Ramadhan and Wahhab, Hassan et al., Albayati and Alani, and Park et al. models. Existing prediction models were calibrated, and to predict the temperature profile of asphalt layer, new models were developed. Performance evaluation and validation of newly developed models showed an excellent correlation between predicted and measured values. Results show the ability of the developed models in predicting the temperature profile of asphalt layers with very good prediction precision (R2 = 0.94) and low bias.
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| số ISSN: | 1392-3730 1822-3605 |