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AuthorArifuzzaman, Md
AuthorGazder, Uneb
AuthorAlam, Md Shah
AuthorSirin, Okan
AuthorAl Mamun, Abdullah
Available date2020-08-12T09:32:57Z
Publication Date2019
Publication NameComputational Intelligence and Neuroscience
ResourceScopus
URIhttp://dx.doi.org/10.1155/2019/3183050
URIhttp://hdl.handle.net/10576/15478
AbstractThe modification by polymers and nanomaterials can significantly improve different properties of asphalt. However, during the service life, the oxidation affects the constituents of modified asphalt and subsequently results in deviation from the desired properties. One of the important properties affected due to oxidation is the adhesive properties of modified asphalt. In this study, the adhesive properties of asphalt modified with the polymers (styrene-butadiene-styrene and styrene-butadiene) and carbon nanotubes were investigated. Asphalt samples were aged in the laboratory by simulating the field conditions, and then adhesive properties were evaluated by different tips of atomic force microscopy (AFM) following the existing functional group in asphalt. Finally, a predictive modelling and machine learning technique called the classification and regression tree (CART) was used to predict the adhesive properties of modified asphalt subjected to oxidation. The parameters that affect the behaviour of asphalt have been used to predict the results using the CART. The results obtained from CART analysis were also compared with those from the regression model. It was observed that the CART analysis shows more explanatory relationships between different variables. The model can predict accurately the adhesive properties of modified asphalts considering the real field oxidation and chemistry of asphalt at a nanoscale.
Sponsorpublication of this article was funded by the Qatar National Library
Languageen
PublisherHindawi Limited
SubjectCART
TitleModelling of Asphalt's Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis
TypeArticle
Volume Number2019
dc.accessType Open Access


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