Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture
Dynamic modulus is a key evaluation index of the high-modulus asphalt mixture, but it is relatively difficult to test and collect its data. The purpose is to achieve the accurate prediction of the dynamic modulus of the high-modulus asphalt mixture and further optimize the design process of the high...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/9944415 |
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| _version_ | 1832561452778520576 |
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| author | Chaohui Wang Songyuan Tan Qian Chen Jiguo Han Liang Song Yi Fu |
| author_facet | Chaohui Wang Songyuan Tan Qian Chen Jiguo Han Liang Song Yi Fu |
| author_sort | Chaohui Wang |
| collection | DOAJ |
| description | Dynamic modulus is a key evaluation index of the high-modulus asphalt mixture, but it is relatively difficult to test and collect its data. The purpose is to achieve the accurate prediction of the dynamic modulus of the high-modulus asphalt mixture and further optimize the design process of the high-modulus asphalt mixture. Five high-temperature performance indexes of high-modulus asphalt and its mixture were selected. The correlation between the above five indexes and the dynamic modulus of the high-modulus asphalt mixture was analyzed. On this basis, the dynamic modulus prediction models of the high-modulus asphalt mixture based on small sample data were established by multiple regression, general regression neural network (GRNN), and support vector machine (SVM) neural network. According to parameter adjustment and cross-validation, the output stability and accuracy of different prediction models were compared and evaluated. The most effective prediction model was recommended. The results show that the SVM model has more significant prediction accuracy and output stability than the multiple regression model and the GRNN model. Its prediction error was 0.98–9.71%. Compared with the other two models, the prediction error of the SVM model declined by 0.50–11.96% and 3.76–13.44%. The SVM neural network was recommended as the dynamic modulus prediction model of the high-modulus asphalt mixture. |
| format | Article |
| id | doaj-art-b1b4b9762ac8426c9f15c38dccb82b5e |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-b1b4b9762ac8426c9f15c38dccb82b5e2025-02-03T01:25:00ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/99444159944415Dynamic Modulus Prediction of a High-Modulus Asphalt MixtureChaohui Wang0Songyuan Tan1Qian Chen2Jiguo Han3Liang Song4Yi Fu5School of Highway, Chang’an University, Xi’an 710064, ChinaSchool of Highway, Chang’an University, Xi’an 710064, ChinaSchool of Highway, Chang’an University, Xi’an 710064, ChinaChina Academy of Transportation Science, Beijing 100129, ChinaXinjiang Transportation Planning Surveying and Design Institute, Urumqi 830006, ChinaSchool of Highway, Chang’an University, Xi’an 710064, ChinaDynamic modulus is a key evaluation index of the high-modulus asphalt mixture, but it is relatively difficult to test and collect its data. The purpose is to achieve the accurate prediction of the dynamic modulus of the high-modulus asphalt mixture and further optimize the design process of the high-modulus asphalt mixture. Five high-temperature performance indexes of high-modulus asphalt and its mixture were selected. The correlation between the above five indexes and the dynamic modulus of the high-modulus asphalt mixture was analyzed. On this basis, the dynamic modulus prediction models of the high-modulus asphalt mixture based on small sample data were established by multiple regression, general regression neural network (GRNN), and support vector machine (SVM) neural network. According to parameter adjustment and cross-validation, the output stability and accuracy of different prediction models were compared and evaluated. The most effective prediction model was recommended. The results show that the SVM model has more significant prediction accuracy and output stability than the multiple regression model and the GRNN model. Its prediction error was 0.98–9.71%. Compared with the other two models, the prediction error of the SVM model declined by 0.50–11.96% and 3.76–13.44%. The SVM neural network was recommended as the dynamic modulus prediction model of the high-modulus asphalt mixture.http://dx.doi.org/10.1155/2021/9944415 |
| spellingShingle | Chaohui Wang Songyuan Tan Qian Chen Jiguo Han Liang Song Yi Fu Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture Advances in Civil Engineering |
| title | Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture |
| title_full | Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture |
| title_fullStr | Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture |
| title_full_unstemmed | Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture |
| title_short | Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture |
| title_sort | dynamic modulus prediction of a high modulus asphalt mixture |
| url | http://dx.doi.org/10.1155/2021/9944415 |
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