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Computational Intelligence and Neuroscience
Volume 2019, Article ID 3183050, 7 pages
Research Article

Modelling of Asphalt’s Adhesive Behaviour Using Classification and Regression Tree (CART) Analysis

1Department of Civil Engineering, University of Bahrain, Zallaq, Bahrain
2Department of Civil and Architectural Engineering, Qatar University, Doha, Qatar

Correspondence should be addressed to Abdullah Al Mamun; aq.ude.uq@numama

Received 19 May 2019; Revised 7 July 2019; Accepted 14 July 2019; Published 15 August 2019

Academic Editor: Amparo Alonso-Betanzos

Copyright © 2019 Md Arifuzzaman et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The publication of this article was funded by Qatar National Library.


The 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.