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...
| Autori principali: | , , |
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| Natura: | Articolo |
| Lingua: | English |
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Vilnius Gediminas Technical University
2023-03-01
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| Serie: | Journal of Civil Engineering and Management |
| Soggetti: | |
| Accesso online: | https://jbem.vgtu.lt/index.php/JCEM/article/view/18611 |
| _version_ | 1827991276810665984 |
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| author | Mohammad Sedighian-Fard Nader Solatifar Henrikas Sivilevičius |
| author_facet | Mohammad Sedighian-Fard Nader Solatifar Henrikas Sivilevičius |
| author_sort | Mohammad Sedighian-Fard |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-04-10T00:49:25Z |
| format | Article |
| id | doaj.art-1ed6bd78a8954cd3b51eb5c47e4ebe40 |
| institution | Directory Open Access Journal |
| issn | 1392-3730 1822-3605 |
| language | English |
| last_indexed | 2024-04-10T00:49:25Z |
| publishDate | 2023-03-01 |
| publisher | Vilnius Gediminas Technical University |
| record_format | Article |
| series | Journal of Civil Engineering and Management |
| spelling | doaj.art-1ed6bd78a8954cd3b51eb5c47e4ebe402023-03-13T16:16:42ZengVilnius Gediminas Technical UniversityJournal of Civil Engineering and Management1392-37301822-36052023-03-0129410.3846/jcem.2023.18611Calibration of regression-based models for prediction of temperature profile of asphalt layers using LTPP dataMohammad Sedighian-Fard0Nader Solatifar1Henrikas Sivilevičius2Department of Civil Engineering, Urmia University, Urmia, IranDepartment of Civil Engineering, Urmia University, Urmia, IranDepartment of Mobile Machinery and Railway Transport, Faculty of Transport Engineering, Vilnius Gediminas Technical University, Vilnius, Lithuania 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. https://jbem.vgtu.lt/index.php/JCEM/article/view/18611asphalt pavementtemperature profile of asphalt layersprediction modelsregression-based modelslong-term pavement performance (LTPP) |
| spellingShingle | Mohammad Sedighian-Fard Nader Solatifar Henrikas Sivilevičius Calibration of regression-based models for prediction of temperature profile of asphalt layers using LTPP data Journal of Civil Engineering and Management asphalt pavement temperature profile of asphalt layers prediction models regression-based models long-term pavement performance (LTPP) |
| title | Calibration of regression-based models for prediction of temperature profile of asphalt layers using LTPP data |
| title_full | Calibration of regression-based models for prediction of temperature profile of asphalt layers using LTPP data |
| title_fullStr | Calibration of regression-based models for prediction of temperature profile of asphalt layers using LTPP data |
| title_full_unstemmed | Calibration of regression-based models for prediction of temperature profile of asphalt layers using LTPP data |
| title_short | Calibration of regression-based models for prediction of temperature profile of asphalt layers using LTPP data |
| title_sort | calibration of regression based models for prediction of temperature profile of asphalt layers using ltpp data |
| topic | asphalt pavement temperature profile of asphalt layers prediction models regression-based models long-term pavement performance (LTPP) |
| url | https://jbem.vgtu.lt/index.php/JCEM/article/view/18611 |
| work_keys_str_mv | AT mohammadsedighianfard calibrationofregressionbasedmodelsforpredictionoftemperatureprofileofasphaltlayersusingltppdata AT nadersolatifar calibrationofregressionbasedmodelsforpredictionoftemperatureprofileofasphaltlayersusingltppdata AT henrikassivilevicius calibrationofregressionbasedmodelsforpredictionoftemperatureprofileofasphaltlayersusingltppdata |