Application of Machine Learning Models to the Analysis of Skid Resistance Data
This paper evaluates the ability of some state-of-the-art Machine Learning models, namely SVM (support vector machines), DT (decision tree) and MLR (multiple linear regression), to predict pavement skid resistance. The study encompasses both regression and classification tasks. In the regression tas...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Lubricants |
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| Online Access: | https://www.mdpi.com/2075-4442/11/8/328 |
| _version_ | 1797584023339401216 |
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| author | Aboubakar Koné Ahmed Es-Sabar Minh-Tan Do |
| author_facet | Aboubakar Koné Ahmed Es-Sabar Minh-Tan Do |
| author_sort | Aboubakar Koné |
| collection | DOAJ |
| description | This paper evaluates the ability of some state-of-the-art Machine Learning models, namely SVM (support vector machines), DT (decision tree) and MLR (multiple linear regression), to predict pavement skid resistance. The study encompasses both regression and classification tasks. In the regression task, the aim is to predict the coefficient of friction values, while the classification task seeks to identify three classes of skid resistance: good, intermediate and bad. The dataset used in this work was gathered through an extensive test campaign that involved a fifth-wheel device to measure the coefficient of friction at different slip ratios on different road surfaces, vehicle speeds, tire tread depths and water depths. It was found that the RBF-SVM model, due to its ability to capture non-linear relationships between the features and the target for a relatively small dataset, is the most adapted tool compared with, on one side, MLR, linear SVM and DT models for the regression task and, on the other side, linear SVM and DT models for the classification task. The paper also discusses the strengths and weaknesses of the investigated models based on the underlying physical phenomena related to skid resistance. |
| first_indexed | 2024-03-10T23:47:21Z |
| format | Article |
| id | doaj.art-43e8cfef120f41e992f0f0166672ab37 |
| institution | Directory Open Access Journal |
| issn | 2075-4442 |
| language | English |
| last_indexed | 2024-03-10T23:47:21Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Lubricants |
| spelling | doaj.art-43e8cfef120f41e992f0f0166672ab372023-11-19T01:55:58ZengMDPI AGLubricants2075-44422023-08-0111832810.3390/lubricants11080328Application of Machine Learning Models to the Analysis of Skid Resistance DataAboubakar Koné0Ahmed Es-Sabar1Minh-Tan Do2AME-EASE, Université Gustave Eiffel, F-44344 Bouguenais, FranceAME-EASE, Université Gustave Eiffel, F-44344 Bouguenais, FranceAME-EASE, Université Gustave Eiffel, F-44344 Bouguenais, FranceThis paper evaluates the ability of some state-of-the-art Machine Learning models, namely SVM (support vector machines), DT (decision tree) and MLR (multiple linear regression), to predict pavement skid resistance. The study encompasses both regression and classification tasks. In the regression task, the aim is to predict the coefficient of friction values, while the classification task seeks to identify three classes of skid resistance: good, intermediate and bad. The dataset used in this work was gathered through an extensive test campaign that involved a fifth-wheel device to measure the coefficient of friction at different slip ratios on different road surfaces, vehicle speeds, tire tread depths and water depths. It was found that the RBF-SVM model, due to its ability to capture non-linear relationships between the features and the target for a relatively small dataset, is the most adapted tool compared with, on one side, MLR, linear SVM and DT models for the regression task and, on the other side, linear SVM and DT models for the classification task. The paper also discusses the strengths and weaknesses of the investigated models based on the underlying physical phenomena related to skid resistance.https://www.mdpi.com/2075-4442/11/8/328skid resistancemachine learningregressionclassificationSVMdecision tree |
| spellingShingle | Aboubakar Koné Ahmed Es-Sabar Minh-Tan Do Application of Machine Learning Models to the Analysis of Skid Resistance Data Lubricants skid resistance machine learning regression classification SVM decision tree |
| title | Application of Machine Learning Models to the Analysis of Skid Resistance Data |
| title_full | Application of Machine Learning Models to the Analysis of Skid Resistance Data |
| title_fullStr | Application of Machine Learning Models to the Analysis of Skid Resistance Data |
| title_full_unstemmed | Application of Machine Learning Models to the Analysis of Skid Resistance Data |
| title_short | Application of Machine Learning Models to the Analysis of Skid Resistance Data |
| title_sort | application of machine learning models to the analysis of skid resistance data |
| topic | skid resistance machine learning regression classification SVM decision tree |
| url | https://www.mdpi.com/2075-4442/11/8/328 |
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