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

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dc.citedby0
dc.contributor.authorJegatheesan N.en_US
dc.contributor.authorIbrahim M.R.en_US
dc.contributor.authorAhmed A.N.en_US
dc.contributor.authorKoting S.en_US
dc.contributor.authorEl-Shafie A.en_US
dc.contributor.authorKatman H.Y.B.en_US
dc.contributor.authorid57503097000en_US
dc.contributor.authorid57872447200en_US
dc.contributor.authorid57214837520en_US
dc.contributor.authorid55839645200en_US
dc.contributor.authorid16068189400en_US
dc.contributor.authorid55812804800en_US
dc.date.accessioned2025-03-03T07:41:54Z
dc.date.available2025-03-03T07:41:54Z
dc.date.issued2024
dc.description.abstractThis 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 Ltden_US
dc.description.natureFinalen_US
dc.identifier.ArtNo137648
dc.identifier.doi10.1016/j.conbuildmat.2024.137648
dc.identifier.scopus2-s2.0-85201273830
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85201273830&doi=10.1016%2fj.conbuildmat.2024.137648&partnerID=40&md5=084db763c1725a366c53eb08fee69f6b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36314
dc.identifier.volume444
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleConstruction and Building Materials
dc.subjectRegression analysis
dc.subjectComposite modification
dc.subjectCrosslinking additive
dc.subjectCrumb rubber
dc.subjectHigh interaction parameter
dc.subjectInteraction parameters
dc.subjectMachine learning algorithms
dc.subjectModified bitumen
dc.subjectPrediction modelling
dc.subjectTerminal blend-crumb rubber modified bitumen
dc.subjectTerminal blends
dc.subjectPrediction models
dc.titleModeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additivesen_US
dc.typeArticleen_US
dspace.entity.typePublication
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