Neural network modelling of asphalt adhesion determined by AFM

Tarefder, Rafiqul A.; Ahsan, Sanjida

doi:10.1111/jmi.12113

Cited by 15 publications

(5 citation statements)

References 17 publications

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“…Tarefder and Ahsan developed the MLP model to predict the adhesion forces of asphalts modified by different factors under nanoscale evaluation of asphalts by AFM. The obtained adhesive force from the model was very satisfactory [17]. It was also found that the adhesion force is significantly affected by asphalt chemistry.…”

Section: Previous Studies On Neural Network Modellingmentioning

confidence: 66%

Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model

Arifuzzaman

Gazder

Islam

et al. 2019

Journal of Adhesion Science and Technology

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The 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 knowledge of the adhesive property of the asphalt can help in providing more durable asphalt pavement. In this regard, this study attempted to develop different models of adhesive properties of polymers and carbon nanotubes (CNTs) modified asphalt binder. 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 asses 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 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.

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Section: Previous Studies On Neural Network Modellingmentioning

confidence: 66%

Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model

Arifuzzaman

Gazder

Islam

et al. 2019

Journal of Adhesion Science and Technology

View full text Add to dashboard Cite

show abstract

“…And, indeed, some improvement in adhesion force was noticed at nanoscale where the sample studied was modified by some hydrocarbon additives that can be seen in Rebelo's study [22]. Also, Tarefder et al [56,57] found this adhesion force enhancement occurred, in general, when asphalt was modified with common styrene-butadiene (SB), SBS polymers, and limes.…”

Section: Adhessibility Of Asphalt Phasesmentioning

confidence: 78%

A Synthesis of Computational and Experimental Approaches of Evaluating Chemical, Physical, and Mechanistic Properties of Asphalt Binders

Hossain

2019

Advances in Civil Engineering

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Asphalt binder is a very complex chemical compound. Much work has been done to understand and model its chemical, morphological, rheological, and mechanical features. This paper synthesizes and presents findings from pertinent studies available in the public domain. Understanding of asphalt characteristics at the very finite level is the first critical step to develop a better macroscopic- or pavement-level performance model. This paper showcases a summary of current knowledge gained on (a) how chemical elemental compositions and molecular groups play critical roles in asphalt binder’s performance in pavement composites; (b) morphological properties and their relationships with the asphalt’s structural performance; and (c) mechanistic characteristics of asphalt binder’s at nanoscopic, mesoscopic, and microscopic levels, and how they are related to macroscopic- or pavement-level performance.

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“…However, there was no drastic change in the error values which indicates that the model did not overfit the training samples. Different artificial intelligence (AI) techniques, such as multilayer perceptions (MLPs), support vector machines (SVMs), and adaptive network fuzzy inference systems (ANFISs), have been used for predicting adhesion force for asphalt [46]. However, for pavement design, these techniques cannot explain the relationship between the variables considered in the study, which makes the use of the proposed model in decision-making difficult.…”

Section: Resultsmentioning

confidence: 99%

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

Arifuzzaman

Gazder

Alam

et al. 2019

Computational Intelligence and Neuroscience

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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.

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Neural network modelling of asphalt adhesion determined by AFM

Cited by 15 publications

References 17 publications

Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model

Prediction and sensitivity analysis of CNTs-modified asphalt’s adhesion force using a radial basis neural network model

A Synthesis of Computational and Experimental Approaches of Evaluating Chemical, Physical, and Mechanistic Properties of Asphalt Binders

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

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