Using a Neural Network Model to Assess the Effect of Antistripping Agents on the Performance of Moisture-Conditioned Asphalt

Tarefder, Rafiqul A.; Ahsan, Sanjida; Arifuzzaman, Md

doi:10.1061/(asce)mt.1943-5533.0001777

Cited by 8 publications

(6 citation statements)

References 9 publications

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“…In these experiments, a total of 290 AFM laboratory tests were accomplished. A detailed description of the whole testing and procedures can be found in Tarefder et al [15].…”

Section: Methodsmentioning

confidence: 99%

“…RMSE has a squaring mechanism to magnify large errors while suppressing smaller errors, MAPE relates the magnitude of error to the magnitude of the observed value, and lastly, CC evaluates the closeness of predicted values' trend to that of the observed ones. These measures have been used in different studies to evaluate accuracy of ANN models [15,32,34]. They were calculated as per Equations ( 3)- (5).…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Laboratory tests have been performed to assess the effect of ASAs on the adhesion and cohesion bonds of asphalt [11][12][13][14]. A nano level experiment was performed in the laboratory with Atomic Force Microscopy (AFM) to determine the effects of ASAs in terms of adhesive and cohesive forces [15,16]. The study simulated adhesion and cohesion of asphalt by using functionalized tips to probe the asphalt sample.…”

Section: Introductionmentioning

confidence: 99%

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Behavioral Investigation of Single Wall and Double Wall CNT Mixed Asphalt Adhesion Force Using Chemical Force Microscopy and Artificial Neural Networks

et al. 2022

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Flexible pavement deterioration due to moisture intrusion and aging is the key concern worldwide for highway engineers. However, this damage has not been properly investigated in detail due to lack of appropriate experimental and modeling techniques. Such lacking hinders the design of long-lasting pavements, as the effects of environmental damages are unknown, especially for modified asphalt. Therefore, the current study aims at determining a better approach for modeling asphalt adhesion damage using Artificial Neural Networks (ANNs). The Atomic Force Microscopy (AFM) test was deployed to determine the adhesion and cohesion forces of asphalt samples with varying contents of polymer and Antistripping Agents (ASAs). Two types of ANN models, namely multilayer perceptions (MLPs) and radial basis function neural network (RBFNN), were used in this effort. Two popular modifications, namely ensemble learning and hierarchical modeling, were also engaged to achieve convenient and accurate damage models. The analysis found that RBFNN was better suited for hierarchical models than MLP. RBFNN is preferred for aged and moisture-damaged samples which have less variation in their datasets. Hierarchical models are convenient to apply as they can be applied to any type of asphalt sample. However, they produced a small reduction in accuracy (less than 10%) as compared to other models. The accuracy of the hierarchical model was found to be satisfactory. The ensemble learning approach showed slight improvement in accuracy for all models ranging between 1–3%, i.e., 6–8 nN. This study recommends the use of hierarchical models, developed with ensemble learning, for prediction of asphalt damage. The results of the study will be helpful for researchers and practitioners working on pavement materials for developing prediction models to prepare a better mix design of polymer modified asphalt.

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“…In these experiments, a total of 290 AFM laboratory tests were accomplished. A detailed description of the whole testing and procedures can be found in Tarefder et al [15].…”

Section: Methodsmentioning

confidence: 99%

Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Behavioral Investigation of Single Wall and Double Wall CNT Mixed Asphalt Adhesion Force Using Chemical Force Microscopy and Artificial Neural Networks

et al. 2022

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“…The result of this study shows that the simulated results of RBF will be calculated faster and more accurately, however, the MIV is more feasible than simulated results for variable selection [15]. Tarefder in 2016 utilized neural network (ANN) to evaluate the influence of types and value of chemical ant stripping agents (ASAs) on the adhesion force of polymer [16]. The application of PET as an elastomer and its effect on asphalt behavior has been seen in previous studies.…”

Section: Introductionmentioning

confidence: 94%

Numerical and Experimental Investigation on the Modified of Hot Mix Asphalt Concrete Containing Crumb Rubber and Waste Pet

et al. 2020

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The main objective of this research is to investigate the rutting of modified mixtures with two additives of crumb rubber and polyethylene terephthalate (PET).Therefore, ITS method, resilient modulus and Dynamic creep were used to investigate asphalt behaviour with these two additives in this article. Modified blends include ten blends that are made by combining crumb rubber and polyethylene terephthalate in various percentages. The modifiers are combined in two percent 10% and 15% with 60/70 penetration bitumen. Finally, with these mentioned tests, the results of the ten modified samples along with the non-modified one were compared. The results showed that the addition of polyethylene terephthalate increased the viscosity and reduced the density; therefore, addition of more polyethylene terephthalate in the modifier reduces the flow number and, on the other hand, addition of 15% modifier containing polyethylene terephthalate resilient module increases the flow number by about 66%. Ultimately, neural network method was used to predict the result of dynamic creep test; indirect tensile strength and the capability of neural network method have been measured to estimate the laboratory result. According to the results, ANFIS can estimate the laboratory data correctly.

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“…The AFM has also been used to evaluate different nanomechanical properties including adhesion and cohesion of asphalt binders [34]. Some of the studies observed the changes in adhesion of asphalt due to the presence of SBS [35], antistripping agents [36], CNTs [37], etc. In any AFM testing setup, the outer surface of the asphalt binder is simply probed with a sharp and tiny tip.…”

Section: Materials and Proceduresmentioning

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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Using a Neural Network Model to Assess the Effect of Antistripping Agents on the Performance of Moisture-Conditioned Asphalt

Cited by 8 publications

References 9 publications

Behavioral Investigation of Single Wall and Double Wall CNT Mixed Asphalt Adhesion Force Using Chemical Force Microscopy and Artificial Neural Networks

Behavioral Investigation of Single Wall and Double Wall CNT Mixed Asphalt Adhesion Force Using Chemical Force Microscopy and Artificial Neural Networks

Numerical and Experimental Investigation on the Modified of Hot Mix Asphalt Concrete Containing Crumb Rubber and Waste Pet

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

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