Asphalt pavement rutting model in seasonal frozen area
Effective prediction of rutting diseases in seasonal frozen area is helpful for comprehensive evaluation of asphalt pavement performance. In this paper, based on the Mechanical-Experienced Pavement Design Guide (MEPDG) theory, the rutting prediction model of asphalt pavement in the seasonal frozen a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Peter the Great St. Petersburg Polytechnic University
2022-07-01
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| Series: | Magazine of Civil Engineering |
| Subjects: | |
| Online Access: | http://engstroy.spbstu.ru/article/2022.112.05/ |
| _version_ | 1818041726536777728 |
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| author | Zhang Lina He Dongpo Zhao Qianqian |
| author_facet | Zhang Lina He Dongpo Zhao Qianqian |
| author_sort | Zhang Lina |
| collection | DOAJ |
| description | Effective prediction of rutting diseases in seasonal frozen area is helpful for comprehensive evaluation of asphalt pavement performance. In this paper, based on the Mechanical-Experienced Pavement Design Guide (MEPDG) theory, the rutting prediction model of asphalt pavement in the seasonal frozen area is established by using the measured rutting data of 9 typical highways in the seasonal frozen area of China. The research results show that the traffic volume, climate, and asphalt layer thickness of the pavement structure are directly proportional to the change in rutting. The proposed correction coefficients for the prediction model of asphalt pavement rutting in the seasonal frozen area are β1r = 2, β2r = 1.03 and β3r = 0.93. The normal distribution map and P-P map of the rutting prediction model conform to the normal distribution. The fit between the predicted data of the prediction model and the measured data is high. The fitting value between the predicted data and the measured data before correction is R2 = 0.9357. The fitting value between the revised predicted data and the measured data is R2 = 0.9925. The research results are of great significance for the prediction of rutting and maintenance of asphalt pavement in the seasonal frozen area. |
| first_indexed | 2024-12-10T08:35:00Z |
| format | Article |
| id | doaj.art-d1fee665b7b244b086c88f22e2e90a96 |
| institution | Directory Open Access Journal |
| issn | 2712-8172 |
| language | English |
| last_indexed | 2024-12-10T08:35:00Z |
| publishDate | 2022-07-01 |
| publisher | Peter the Great St. Petersburg Polytechnic University |
| record_format | Article |
| series | Magazine of Civil Engineering |
| spelling | doaj.art-d1fee665b7b244b086c88f22e2e90a962022-12-22T01:56:00ZengPeter the Great St. Petersburg Polytechnic UniversityMagazine of Civil Engineering2712-81722022-07-011120410.34910/MCE.112.520714726Asphalt pavement rutting model in seasonal frozen areaZhang Lina0https://orcid.org/0000-0002-2024-3806He Dongpo1https://orcid.org/0000-0003-2427-1086Zhao Qianqian2https://orcid.org/0000-0002-0209-4181Northeast Forestry UniversityNortheast Forestry UniversityNortheast Agricultural UniversityEffective prediction of rutting diseases in seasonal frozen area is helpful for comprehensive evaluation of asphalt pavement performance. In this paper, based on the Mechanical-Experienced Pavement Design Guide (MEPDG) theory, the rutting prediction model of asphalt pavement in the seasonal frozen area is established by using the measured rutting data of 9 typical highways in the seasonal frozen area of China. The research results show that the traffic volume, climate, and asphalt layer thickness of the pavement structure are directly proportional to the change in rutting. The proposed correction coefficients for the prediction model of asphalt pavement rutting in the seasonal frozen area are β1r = 2, β2r = 1.03 and β3r = 0.93. The normal distribution map and P-P map of the rutting prediction model conform to the normal distribution. The fit between the predicted data of the prediction model and the measured data is high. The fitting value between the predicted data and the measured data before correction is R2 = 0.9357. The fitting value between the revised predicted data and the measured data is R2 = 0.9925. The research results are of great significance for the prediction of rutting and maintenance of asphalt pavement in the seasonal frozen area.http://engstroy.spbstu.ru/article/2022.112.05/deteriorationpavement maintenancedesign model |
| spellingShingle | Zhang Lina He Dongpo Zhao Qianqian Asphalt pavement rutting model in seasonal frozen area Magazine of Civil Engineering deterioration pavement maintenance design model |
| title | Asphalt pavement rutting model in seasonal frozen area |
| title_full | Asphalt pavement rutting model in seasonal frozen area |
| title_fullStr | Asphalt pavement rutting model in seasonal frozen area |
| title_full_unstemmed | Asphalt pavement rutting model in seasonal frozen area |
| title_short | Asphalt pavement rutting model in seasonal frozen area |
| title_sort | asphalt pavement rutting model in seasonal frozen area |
| topic | deterioration pavement maintenance design model |
| url | http://engstroy.spbstu.ru/article/2022.112.05/ |
| work_keys_str_mv | AT zhanglina asphaltpavementruttingmodelinseasonalfrozenarea AT hedongpo asphaltpavementruttingmodelinseasonalfrozenarea AT zhaoqianqian asphaltpavementruttingmodelinseasonalfrozenarea |