Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems
Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/7/1718 |
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| author | Nader Karballaeezadeh Farah Zaremotekhases Shahaboddin Shamshirband Amir Mosavi Narjes Nabipour Peter Csiba Annamária R. Várkonyi-Kóczy |
| author_facet | Nader Karballaeezadeh Farah Zaremotekhases Shahaboddin Shamshirband Amir Mosavi Narjes Nabipour Peter Csiba Annamária R. Várkonyi-Kóczy |
| author_sort | Nader Karballaeezadeh |
| collection | DOAJ |
| description | Prediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SE). The CMIS model outperforms other models with the promising results of APRE = 2.3303, AAPRE = 11.6768, RMSE = 12.0056 and SD = 0.0210. |
| first_indexed | 2024-03-10T20:40:41Z |
| format | Article |
| id | doaj.art-17bcdcf0694d4cd8a15954dc63d25ae0 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T20:40:41Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-17bcdcf0694d4cd8a15954dc63d25ae02023-11-19T20:41:55ZengMDPI AGEnergies1996-10732020-04-01137171810.3390/en13071718Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance SystemsNader Karballaeezadeh0Farah Zaremotekhases1Shahaboddin Shamshirband2Amir Mosavi3Narjes Nabipour4Peter Csiba5Annamária R. Várkonyi-Kóczy6Faculty of Civil Engineering, Shahrood University of Technology, Shahrood 3619995161, IranDepartment of Construction Management, Louisiana State University, Baton Rouge, LA 70803, USADepartment for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, VietnamThuringian Institute of Sustainability and Climate Protection, 07743 Jena, GermanyInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamDepartment of Mathematics and Informatics, J. Selye University, 94501 Komarno, SlovakiaDepartment of Mathematics and Informatics, J. Selye University, 94501 Komarno, SlovakiaPrediction models in mobility and transportation maintenance systems have been dramatically improved by using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well as their hybrids, i.e., Levenberg–Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE) and standard error (SE). The CMIS model outperforms other models with the promising results of APRE = 2.3303, AAPRE = 11.6768, RMSE = 12.0056 and SD = 0.0210.https://www.mdpi.com/1996-1073/13/7/1718transportationmobilityprediction modelmachine learningpavement managementpavement condition index |
| spellingShingle | Nader Karballaeezadeh Farah Zaremotekhases Shahaboddin Shamshirband Amir Mosavi Narjes Nabipour Peter Csiba Annamária R. Várkonyi-Kóczy Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems Energies transportation mobility prediction model machine learning pavement management pavement condition index |
| title | Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems |
| title_full | Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems |
| title_fullStr | Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems |
| title_full_unstemmed | Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems |
| title_short | Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems |
| title_sort | intelligent road inspection with advanced machine learning hybrid prediction models for smart mobility and transportation maintenance systems |
| topic | transportation mobility prediction model machine learning pavement management pavement condition index |
| url | https://www.mdpi.com/1996-1073/13/7/1718 |
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