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AuthorArifuzzaman, Md
AuthorGazder, Uneb
AuthorIslam, Muhammad Saiful
AuthorAl Mamun, Abdullah
Available date2020-08-18T08:34:44Z
Publication Date2019
Publication NameJournal of Adhesion Science and Technology
ResourceScopus
ISSN1694243
URIhttp://dx.doi.org/10.1080/01694243.2019.1698201
URIhttp://hdl.handle.net/10576/15644
AbstractThe expected longer service life of modified asphalt can be jeopardized by different environmental factors, such as moisture, oxidation, etc. which affect the desired properties by altering the adhesive property. An insight into knowledge of the adhesive property of the asphalt can help in providing more durable asphalt pavement. The study attempted to develop different models of adhesive properties of polymers and carbon nanotubes (CNTs) modified asphalt binders. The polymer-CNT modified asphalt is processed to prepare different types of samples, by simulating the damage due to moisture and oxidization, following the corresponding standard method. An Atomic Force Microscopy (AFM) was employed to assess the nanoscale adhesion force of the tested samples following the existing functional group in asphalt. Finally, the study has developed Radial Basis Function Neural Network (RBFNN) as a function of different parameters including; asphalt chemistry (i.e. AFM tip type and constant), type and percentages of polymers and CNTs and different environmental exposures (oxidation, moisture, etc.) to predict the nano adhesion force of asphalt. It is observed that the adhesive property of the Styrene�Butadiene modified asphalt is more consistent compared to the Styrene�Butadiene�Styrene modified asphalt, while the presence of Single-Wall Nanotubes (SWNT) is observed to affect the adhesive properties of asphalt significantly as compared to Multi-Wall Nanotubes (MWNT). The higher accuracy level of RBFNN model also indicates that the functional group (tip-type) adding with the percentages and types of polymers and CNTs significantly affect the adhesive properties of asphalt. - 2019, - 2019 Informa UK Limited, trading as Taylor & Francis Group.
Languageen
PublisherTaylor and Francis Ltd.
Subjectadhesion
Asphalt
atomic force microscopy
carbon nanotubes
radial basis function neural network
TitlePrediction and sensitivity analysis of CNTs-modified asphalt's adhesion force using a radial basis neural network model
TypeArticle
dc.accessType Abstract Only


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