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Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives

This study aimed to develop models assessing 26 machine-learning algorithms in regression analysis to predict the properties of terminal blend crumb rubber-modified bitumen (TB-CRMB) made with crosslinking additives. During the data collection, the properties of the modified binders prepared at 6, 1...

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Main Authors:	Jegatheesan N., Ibrahim M.R., Ahmed A.N., Koting S., El-Shafie A., Katman H.Y.B.
Other Authors:	57503097000
Format:	Article
Published:	Elsevier Ltd 2025
Subjects:	Regression analysis Composite modification Crosslinking additive Crumb rubber High interaction parameter Interaction parameters Machine learning algorithms Modified bitumen Prediction modelling Terminal blend-crumb rubber modified bitumen Terminal blends Prediction models
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id	my.uniten.dspace-36314
record_format	dspace
spelling	my.uniten.dspace-363142025-03-03T15:41:54Z Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives Jegatheesan N. Ibrahim M.R. Ahmed A.N. Koting S. El-Shafie A. Katman H.Y.B. 57503097000 57872447200 57214837520 55839645200 16068189400 55812804800 Regression analysis Composite modification Crosslinking additive Crumb rubber High interaction parameter Interaction parameters Machine learning algorithms Modified bitumen Prediction modelling Terminal blend-crumb rubber modified bitumen Terminal blends Prediction models This study aimed to develop models assessing 26 machine-learning algorithms in regression analysis to predict the properties of terminal blend crumb rubber-modified bitumen (TB-CRMB) made with crosslinking additives. During the data collection, the properties of the modified binders prepared at 6, 10 and 14% of crumb rubber (CR), considering three types of modifications and eighteen blending scenarios with different interaction factors, were assessed in terms of penetration, softening point, rotational viscosity, storage stability, rheological parameters, and rutting and fatigue factors. Results showed that the Matern 5/2 Gaussian Process Regression (GPR) model demonstrated efficient performance in predicting physical, viscoelastic, rutting, and fatigue properties whereas wide artificial neural networks exhibited enhanced accuracy in predicting storage stability and rotational viscosity. The results also suggest that it is feasible to implement a single type of model developed using the Matern 5/2 GPR algorithm for accurately predicting all the TB-CRMB properties considered. The best models demonstrated that crosslinking additives significantly influenced TB-CRMB production and performance. In TB-CRMB production, sulfur as a crosslinking additive showed better compatibility than trans-polyoctenamer-rubber and significantly reduced interaction temperatures at lower CR content, leading to energy savings compared to the traditional TB production. ? 2024 Elsevier Ltd Final 2025-03-03T07:41:54Z 2025-03-03T07:41:54Z 2024 Article 10.1016/j.conbuildmat.2024.137648 2-s2.0-85201273830 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201273830&doi=10.1016%2fj.conbuildmat.2024.137648&partnerID=40&md5=084db763c1725a366c53eb08fee69f6b https://irepository.uniten.edu.my/handle/123456789/36314 444 137648 Elsevier Ltd Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Regression analysis Composite modification Crosslinking additive Crumb rubber High interaction parameter Interaction parameters Machine learning algorithms Modified bitumen Prediction modelling Terminal blend-crumb rubber modified bitumen Terminal blends Prediction models
spellingShingle	Regression analysis Composite modification Crosslinking additive Crumb rubber High interaction parameter Interaction parameters Machine learning algorithms Modified bitumen Prediction modelling Terminal blend-crumb rubber modified bitumen Terminal blends Prediction models Jegatheesan N. Ibrahim M.R. Ahmed A.N. Koting S. El-Shafie A. Katman H.Y.B. Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives
description	This study aimed to develop models assessing 26 machine-learning algorithms in regression analysis to predict the properties of terminal blend crumb rubber-modified bitumen (TB-CRMB) made with crosslinking additives. During the data collection, the properties of the modified binders prepared at 6, 10 and 14% of crumb rubber (CR), considering three types of modifications and eighteen blending scenarios with different interaction factors, were assessed in terms of penetration, softening point, rotational viscosity, storage stability, rheological parameters, and rutting and fatigue factors. Results showed that the Matern 5/2 Gaussian Process Regression (GPR) model demonstrated efficient performance in predicting physical, viscoelastic, rutting, and fatigue properties whereas wide artificial neural networks exhibited enhanced accuracy in predicting storage stability and rotational viscosity. The results also suggest that it is feasible to implement a single type of model developed using the Matern 5/2 GPR algorithm for accurately predicting all the TB-CRMB properties considered. The best models demonstrated that crosslinking additives significantly influenced TB-CRMB production and performance. In TB-CRMB production, sulfur as a crosslinking additive showed better compatibility than trans-polyoctenamer-rubber and significantly reduced interaction temperatures at lower CR content, leading to energy savings compared to the traditional TB production. ? 2024 Elsevier Ltd
author2	57503097000
author_facet	57503097000 Jegatheesan N. Ibrahim M.R. Ahmed A.N. Koting S. El-Shafie A. Katman H.Y.B.
format	Article
author	Jegatheesan N. Ibrahim M.R. Ahmed A.N. Koting S. El-Shafie A. Katman H.Y.B.
author_sort	Jegatheesan N.
title	Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives
title_short	Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives
title_full	Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives
title_fullStr	Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives
title_full_unstemmed	Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives
title_sort	modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives
publisher	Elsevier Ltd
publishDate	2025
_version_	1825816018758402048
score	13.244109

Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives

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