Pavement Deterioration Modeling for Forest Roads Based on Logistic Regression and Artificial Neural Networks
The accurate prediction of forest road pavement performance is important for efficient management of surface transportation infrastructure and achieves significant savings through timely intervention and accurate planning. The aim of this paper was to introduce a methodology for developing accurate...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
University of Zagreb, Faculty of Forestry
2018-01-01
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| Series: | Croatian Journal of Forest Engineering |
| Online Access: | https://hrcak.srce.hr/file/300558 |
| _version_ | 1818150548034027520 |
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| author | Mohammad Javad Heidari Akbar Najafi Seyedjalil Alavi |
| author_facet | Mohammad Javad Heidari Akbar Najafi Seyedjalil Alavi |
| author_sort | Mohammad Javad Heidari |
| collection | DOAJ |
| description | The accurate prediction of forest road pavement performance is important for efficient management of surface transportation infrastructure and achieves significant savings through timely intervention and accurate planning. The aim of this paper was to introduce a methodology for developing accurate pavement deterioration models to be used primarily for the management of the forest road infrastructure. For this purpose, 19 explanatory and three corresponding response variables were measured in 185 segments of 50 km forest roads. Logistic regression (LR) and artificial neural networks (ANNs) were used to predict forest road pavement deterioration, Pothole, rutting and protrusion, as a function of pavement condition, environmental factors, traffic and road qualify. The results showed ANNs and LR models could classify from 82% to 89% of the current pavement condition correctly. According to the results, LR model and ANNs predicted rutting, pothole and protrusion with 83.5%, 83.00% and 81.75%, 88.65% and 85.20%, 80.00% accuracy. Equivalent single axle load (ESAL), date of repair, thickness of pavement and slope were identified as most significant explanatory variables. Receiver Operating Characteristic Curve (ROC) showed that the results obtained by ANNs and logistic regression are close to each other. |
| first_indexed | 2024-12-11T13:24:41Z |
| format | Article |
| id | doaj.art-31958f2966b64ac992a63e0d8fbeb4d3 |
| institution | Directory Open Access Journal |
| issn | 1845-5719 1848-9672 |
| language | English |
| last_indexed | 2024-12-11T13:24:41Z |
| publishDate | 2018-01-01 |
| publisher | University of Zagreb, Faculty of Forestry |
| record_format | Article |
| series | Croatian Journal of Forest Engineering |
| spelling | doaj.art-31958f2966b64ac992a63e0d8fbeb4d32022-12-22T01:05:37ZengUniversity of Zagreb, Faculty of ForestryCroatian Journal of Forest Engineering1845-57191848-96722018-01-01392271287204195Pavement Deterioration Modeling for Forest Roads Based on Logistic Regression and Artificial Neural NetworksMohammad Javad Heidari0Akbar Najafi1Seyedjalil Alavi2Tarbiat Modares University Faculty of Natural Resources & Marine Sciences P.O.Box: 14115-111 Tehran IRANTarbiat Modares University Faculty of Natural Resources & Marine Sciences P.O.Box: 14115-111 Tehran IRANTarbiat Modares University Faculty of Natural Resources & Marine Sciences P.O.Box: 14115-111 Tehran IRANThe accurate prediction of forest road pavement performance is important for efficient management of surface transportation infrastructure and achieves significant savings through timely intervention and accurate planning. The aim of this paper was to introduce a methodology for developing accurate pavement deterioration models to be used primarily for the management of the forest road infrastructure. For this purpose, 19 explanatory and three corresponding response variables were measured in 185 segments of 50 km forest roads. Logistic regression (LR) and artificial neural networks (ANNs) were used to predict forest road pavement deterioration, Pothole, rutting and protrusion, as a function of pavement condition, environmental factors, traffic and road qualify. The results showed ANNs and LR models could classify from 82% to 89% of the current pavement condition correctly. According to the results, LR model and ANNs predicted rutting, pothole and protrusion with 83.5%, 83.00% and 81.75%, 88.65% and 85.20%, 80.00% accuracy. Equivalent single axle load (ESAL), date of repair, thickness of pavement and slope were identified as most significant explanatory variables. Receiver Operating Characteristic Curve (ROC) showed that the results obtained by ANNs and logistic regression are close to each other.https://hrcak.srce.hr/file/300558 |
| spellingShingle | Mohammad Javad Heidari Akbar Najafi Seyedjalil Alavi Pavement Deterioration Modeling for Forest Roads Based on Logistic Regression and Artificial Neural Networks Croatian Journal of Forest Engineering |
| title | Pavement Deterioration Modeling for Forest Roads Based on Logistic Regression and Artificial Neural Networks |
| title_full | Pavement Deterioration Modeling for Forest Roads Based on Logistic Regression and Artificial Neural Networks |
| title_fullStr | Pavement Deterioration Modeling for Forest Roads Based on Logistic Regression and Artificial Neural Networks |
| title_full_unstemmed | Pavement Deterioration Modeling for Forest Roads Based on Logistic Regression and Artificial Neural Networks |
| title_short | Pavement Deterioration Modeling for Forest Roads Based on Logistic Regression and Artificial Neural Networks |
| title_sort | pavement deterioration modeling for forest roads based on logistic regression and artificial neural networks |
| url | https://hrcak.srce.hr/file/300558 |
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