Novel ensemble modelling for prediction of fundamental properties of bitumen incorporating plastic waste
Plastic asphalt mixtures (PAMs) have garnered attention recently, but their field application has been limited due to a lack of understanding of asphalt mix behavior following modification. A modelling tool that can calculate the plastic influence on the characteristics of asphalt mixtures is requir...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Journal of Materials Research and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2238785423006816 |
| _version_ | 1797798814107566080 |
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| author | Md Arifuzzaman Hisham Jahangir Qureshi Abdulrahman Fahad Al Fuhaid Fayez Alanazi Muhammad Faisal Javed Sayed M. Eldin |
| author_facet | Md Arifuzzaman Hisham Jahangir Qureshi Abdulrahman Fahad Al Fuhaid Fayez Alanazi Muhammad Faisal Javed Sayed M. Eldin |
| author_sort | Md Arifuzzaman |
| collection | DOAJ |
| description | Plastic asphalt mixtures (PAMs) have garnered attention recently, but their field application has been limited due to a lack of understanding of asphalt mix behavior following modification. A modelling tool that can calculate the plastic influence on the characteristics of asphalt mixtures is required to close this gap. Hence, this study offers a performance analysis of various machine learning (ML) models in predicting the performance of PAMs through its various properties. These models include three methods, decision tree (DT) as an individual technique, adaboost regressor (AR), and bagging regressor (BR), as ensemble techniques for prediction of fundamental properties of PAMs i.e. air voids (Va), marshall flow (MF), marshall stability (MS), tensile strength ratio (TSR), and indirect tensile strength (ITS). A series of experimental works and their results on the PAMs properties were collected through literature, to develop ML models and compare their accuracy. The comparative findings demonstrated that the BR model had the largest coefficient of determination (R2) and the lowest statistical errors, making it the model with the best predictive ability. According to the study's findings, ensemble approaches can be effectively utilized to forecast different properties of PAMs. Comparing ensemble models to the single model that served as their base learner, ensemble models were able to increase prediction accuracy. Furthermore, cross validation was performed to check the accuracy of developed models. SHAP analysis was conducted to examine the effects of input parameters on the fundamental properties of PAMs. |
| first_indexed | 2024-03-13T04:09:33Z |
| format | Article |
| id | doaj.art-9c7be7de5d5a467faeae3b0fccf4faf0 |
| institution | Directory Open Access Journal |
| issn | 2238-7854 |
| language | English |
| last_indexed | 2024-03-13T04:09:33Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Materials Research and Technology |
| spelling | doaj.art-9c7be7de5d5a467faeae3b0fccf4faf02023-06-21T06:56:20ZengElsevierJournal of Materials Research and Technology2238-78542023-05-012433343351Novel ensemble modelling for prediction of fundamental properties of bitumen incorporating plastic wasteMd Arifuzzaman0Hisham Jahangir Qureshi1Abdulrahman Fahad Al Fuhaid2Fayez Alanazi3Muhammad Faisal Javed4Sayed M. Eldin5Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi Arabia; Corresponding author.Department of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi ArabiaDepartment of Civil and Environmental Engineering, College of Engineering, King Faisal University, Al-Ahsa, 31982, Saudi ArabiaCivil Engineering Department, College of Engineering, Jouf University, Sakaka, 72238, Saudi ArabiaDepartment of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Abbottabad 22060, PakistanCenter of Research Faculty of Engineering, Future University in Egypt New Cairo 11835, EgyptPlastic asphalt mixtures (PAMs) have garnered attention recently, but their field application has been limited due to a lack of understanding of asphalt mix behavior following modification. A modelling tool that can calculate the plastic influence on the characteristics of asphalt mixtures is required to close this gap. Hence, this study offers a performance analysis of various machine learning (ML) models in predicting the performance of PAMs through its various properties. These models include three methods, decision tree (DT) as an individual technique, adaboost regressor (AR), and bagging regressor (BR), as ensemble techniques for prediction of fundamental properties of PAMs i.e. air voids (Va), marshall flow (MF), marshall stability (MS), tensile strength ratio (TSR), and indirect tensile strength (ITS). A series of experimental works and their results on the PAMs properties were collected through literature, to develop ML models and compare their accuracy. The comparative findings demonstrated that the BR model had the largest coefficient of determination (R2) and the lowest statistical errors, making it the model with the best predictive ability. According to the study's findings, ensemble approaches can be effectively utilized to forecast different properties of PAMs. Comparing ensemble models to the single model that served as their base learner, ensemble models were able to increase prediction accuracy. Furthermore, cross validation was performed to check the accuracy of developed models. SHAP analysis was conducted to examine the effects of input parameters on the fundamental properties of PAMs.http://www.sciencedirect.com/science/article/pii/S2238785423006816Plastic asphalt mixtures (PAMs)Marshall mix parameterEnsemble modellingAir voids (Va)Tensile strength ratio (TSR)Indirect tensile strength (ITS) |
| spellingShingle | Md Arifuzzaman Hisham Jahangir Qureshi Abdulrahman Fahad Al Fuhaid Fayez Alanazi Muhammad Faisal Javed Sayed M. Eldin Novel ensemble modelling for prediction of fundamental properties of bitumen incorporating plastic waste Journal of Materials Research and Technology Plastic asphalt mixtures (PAMs) Marshall mix parameter Ensemble modelling Air voids (Va) Tensile strength ratio (TSR) Indirect tensile strength (ITS) |
| title | Novel ensemble modelling for prediction of fundamental properties of bitumen incorporating plastic waste |
| title_full | Novel ensemble modelling for prediction of fundamental properties of bitumen incorporating plastic waste |
| title_fullStr | Novel ensemble modelling for prediction of fundamental properties of bitumen incorporating plastic waste |
| title_full_unstemmed | Novel ensemble modelling for prediction of fundamental properties of bitumen incorporating plastic waste |
| title_short | Novel ensemble modelling for prediction of fundamental properties of bitumen incorporating plastic waste |
| title_sort | novel ensemble modelling for prediction of fundamental properties of bitumen incorporating plastic waste |
| topic | Plastic asphalt mixtures (PAMs) Marshall mix parameter Ensemble modelling Air voids (Va) Tensile strength ratio (TSR) Indirect tensile strength (ITS) |
| url | http://www.sciencedirect.com/science/article/pii/S2238785423006816 |
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