A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis
Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model includ...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/7534970 |
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| _version_ | 1832556375210721280 |
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| author | Xuancang Wang Jing Zhao Qiqi Li Naren Fang Peicheng Wang Longting Ding Shanqiang Li |
| author_facet | Xuancang Wang Jing Zhao Qiqi Li Naren Fang Peicheng Wang Longting Ding Shanqiang Li |
| author_sort | Xuancang Wang |
| collection | DOAJ |
| description | Pavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction. |
| format | Article |
| id | doaj-art-fc0e93b62c334a44b3d9429b07b09904 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-fc0e93b62c334a44b3d9429b07b099042025-02-03T05:45:45ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/75349707534970A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation AnalysisXuancang Wang0Jing Zhao1Qiqi Li2Naren Fang3Peicheng Wang4Longting Ding5Shanqiang Li6School 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, ChinaSchool of Highway, Chang’an University, Xi’an 710064, ChinaSchool of Information Engineering, Chang’an University, Xi’an 710064, ChinaSchool of Highway, Chang’an University, Xi’an 710064, ChinaSchool of Highway, Chang’an University, Xi’an 710064, ChinaPavement performance prediction is a crucial issue in big data maintenance. This paper develops a hybrid grey relation analysis (GRA) and support vector machine regression (SVR) technique to predict pavement performance. The prediction model can solve the shortcomings of the traditional model including a single consideration factor, a short prediction period, and easy overfitting. GAR is employed in selecting the main factors affecting the performance of asphalt pavement. The SVR is performed to predict the performance. Finally, the data collected from the weather station installed on Guangyun Expressway were adopted to verify the validity of the GRA-SVR model. Meanwhile, the contrast with the grey model (GM (1, 1)), genetic algorithm optimization BP[[parms resize(1),pos(50,50),size(200,200),bgcol(156)]]081%, −0.823%, 1.270%, and −4.569%, respectively. The study concluded that the nonlinear and multivariate prediction model established by GRA-SVR has higher precision and operability, which can be used in long-period pavement performance prediction.http://dx.doi.org/10.1155/2020/7534970 |
| spellingShingle | Xuancang Wang Jing Zhao Qiqi Li Naren Fang Peicheng Wang Longting Ding Shanqiang Li A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis Journal of Advanced Transportation |
| title | A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis |
| title_full | A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis |
| title_fullStr | A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis |
| title_full_unstemmed | A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis |
| title_short | A Hybrid Model for Prediction in Asphalt Pavement Performance Based on Support Vector Machine and Grey Relation Analysis |
| title_sort | hybrid model for prediction in asphalt pavement performance based on support vector machine and grey relation analysis |
| url | http://dx.doi.org/10.1155/2020/7534970 |
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