The addition of polymers and nanomaterials to asphalt can significantly improve the material's various properties.
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In this article, we have tried to analyze the ways and materials to improve asphalt and its performance. Nonetheless, oxidation affects the components that comprise modified asphalt over the course of its service life, resulting in a deviation from the desired properties. The oxidation of modified asphalt has a significant impact on several important properties, including its adhesive properties. The adhesive properties of asphalt modified with polymers (styrene-butadiene-styrene and styrene-butadiene) and carbon nanotubes were investigated in this study. In the laboratory, asphalt samples were aged by simulating field conditions. The adhesive properties of the samples were then examined using various atomic force microscopy (AFM) tips in accordance with the asphalt's preexisting functional groups. Finally, the classification and regression tree, or CART, predictive modeling, and machine learning technique were used to predict the adhesive properties of oxidized modified asphalt. The CART model was used to make predictions based on the results, using the parameters that influence how asphalt behaves. The outcomes of the CART analysis were also compared to the outcomes of the regression model. The CART analysis revealed more explanatory relationships among the variables. The model can make accurate predictions about the adhesive properties of modified asphalts by accounting for actual field oxidation and asphalt chemistry on a nanoscale. Asphalt is typically modified with a variety of polymers by researchers in order to create more durable and environmentally friendly pavements. Polymers with long chains are common in the asphalt modification process (straight or cross-linked). The chemistry and structure of the chains influence the behavior of both the polymer and polymer-modified asphalt (PMA). The most common types of polymers are elastomers and plastomers. While the elastomers work to improve the modified asphalt's elastic properties, the plastomers work to provide the modified asphalt with a plastic matrix. The PMA will only refer to styrene-butadiene-styrene (SBS) and styrene-butadiene (SB) modified asphalt in the following sections due to the scope of this study. SBS, followed by reclaimed tire rubber, is one of the most commonly used polymers in the asphalt industry. This improves the mechanical, physical, and rheological properties of asphalt mixtures increasing asphalt elasticity and tensile properties and decreasing asphalt creep stiffness. The SB product is an SBS block copolymer with elastomer properties. SB has the potential to affect a wide range of asphalt properties, including viscoelastic properties (Jnr) as well as increased resistance to permanent deformation at moderate temperatures (25°C) low-temperature ductility, and others. SB and SBS have been shown to have a significant impact on asphalt adhesive properties, in addition to providing the aforementioned properties on their own. The effectiveness of the improved properties, including the adhesive properties, however, varies over time. Asphalt oxidation is one of the potential factors that could affect the desired properties of PMA. 
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Other factors may also be present. Oxidation has the potential to change the constituents of asphalt as well as its adhesion, which will erode the viscoelastic properties of the asphalt and lead to the failure of the asphalt pavement. As a result, an explanation is absolutely necessary in order to make an accurate prediction about the oxidized PMA's adhesive quality. Aside from the use of polymers, researchers have focused on the introduction of nanomaterials for asphalt modification due to the rapid development of nanotechnology, increased traffic, and the desire for improved asphalt pavement properties. Nanomaterials are materials with at least one dimension between one and one hundred nanometers. The increased surface-to-volume ratio, as well as the nanometer-sized plates, contribute to nano-sized particles having properties distinct from those of conventional materials. In addition, nanomaterials have been discovered to have high-temperature sensitivity, high ductility, a large surface area, high tension resistance, low electrical resistance, and a variety of other properties. Carbon nanotubes, or CNTs, are just one of the many nanomaterials used in the asphalt modification process. This is because these nanomaterials have advantageous properties. CNTs have been shown to improve tracking resistance and thermal cracking. 
Asphalt Adhesive Material It has the potential to improve the rheological and adhesive properties of asphalt significantly, and an increase in CNT content can result in highly viscous and elastic coefficient values regardless of binder type. The adhesive properties of asphalt, on the other hand, are altered when nanomaterials, also known as filler materials, are present. Furthermore, oxidation can influence the adhesive properties of carbon nanotube-modified asphalt (CMA). As a result, visualizing and anticipating changes in the adhesive properties of oxidized CMA is critical. A sample of fresh asphalt was heated to approximately 164 degrees Celsius in a laboratory container. After thirty to forty-five minutes, the asphalt binder was combined with the selected polymer fraction and the CNT. Dry-conditioned samples are those that have been modified after being mixed with CNT and polymer. The samples were placed on a glass substrate that was 10 millimeters on each side and 1 millimeter on top. To simulate the expected level of aging that would occur in the field, the dry samples were aged for seven days in an oven with a draft and an elevated temperature of sixty degrees Celsius. Given what has been said thus far, it should come as no surprise that oxidized asphalt has important adhesive properties. This is true regardless of the modified asphalt used (PMA or CMA). To the best of the authors' knowledge, no previous studies attempted to predict the adhesive properties of oxidized asphalt. Studies on the adhesive properties of PMA or CMA, or focused on the effect of moisture rather than the effect of oxidation. In this regard, the classification and regression tree, predictive modeling, and machine learning technique, were used in this study to forecast the adhesive properties of oxidized asphalt modified with polymers and CNTs (CART). By simulating asphalt oxidation and chemistry on a nanoscale, the model considers the adhesive properties of modified asphalt. To gain a better understanding of the effect of various factors on asphalt adhesion force, both the multiple regression model and the classification and regression tree (CART) approaches were used. 
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As this is a parametric technique, one must first make an assumption about the fundamental structure of the data before using regression models. However, despite its inability to explain anything, a nonparametric technique such as artificial neural networks (ANNs) has been used. The CART method is a nonparametric approach that allows variables to be included in multiple stages of the tree simultaneously. As a result, complex interdependencies among variables can be discovered. By utilizing its adaptive interpretation capabilities, the CART was able to successfully deal with the complicated nonlinearity that existed between the predictors and the response. It is better suited to dealing with multicollinearity issues presented by data than regression models. Furthermore, the CART analysis provides a model that can be interpreted using logical statements to understand how various variables affect the target variable. This model is not commonly found in other data mining tools [40], which distinguishes the CART analysis. Not only was the CART application successful in terms of understanding and predicting consumer behavior, but it was also successful in terms of road safety research. It was also used in a variety of pavement engineering fields, such as the evaluation of the field serviceability of pothole patches, the factors influencing the permeability of rigid pavement, and the roughness of asphalt pavement, the field prediction of maintenance probability, and the selection of specific maintenance approach based on the existing condition. However, very few studies have been found to use predictive models to predict asphalt adhesion force, and the CART technique has not been found to be used in any of these studies. As a result, this method was used to predict the behavior of oxidized asphalt for the first time. 
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The accuracy was calculated by multiplying the coefficient of correlation, the root means square error (RMSE), and the mean absolute percentage error (MAPE) by the number of samples used for training and testing the model. These values were calculated by combining the laboratory experiment's actual/target values with the model's predictions. The coefficient of correlation (CC) was acceptable for both the training and test samples. This is something that can be seen. The test samples' mean absolute percentage error (MAPE) was approximately 27%, which translates to 53 kN (root mean square error; RMSE) in terms of adhesion force. The CART's accuracy measurements were found to be adequate for both training and test samples. However, the error values remained stable, indicating that the model did not overfit the training samples. To predict the adhesion force for asphalt, various artificial intelligence (AI) techniques such as multilayer perceptions (MLPs), support vector machines (SVMs), and adaptive network fuzzy inference systems (ANFISs) have been used. However, these techniques are incapable of explaining the relationship between the variables considered in the study for the purposes of pavement design. As a result, implementing the proposed model in decision-making is challenging. This gap can be filled by the CART, which explains variable relationships. 
Improve Asphalt Adhesion The tip type, specifically NH3, which is located at the very top of the tree, was discovered to be the most significant parameter. The addition of NH3 to the tip improves asphalt adhesion. As a result, before the results of AFM tests can be used for mixed design, they must be designed properly. The NH3 tip and SBS5 were determined to be the best binders to use because this node has the highest mean adhesion force. This resulted in the strongest adhesion force. When used with any tip type other than CH3, it was discovered that the SBS5 binder increased the adhesion force to a greater extent. As a result, having dual styrene bonds increases asphalt adhesion force, which can be attributed to the additive having a higher degree of internal bonding. This is because asphalt with dual styrene bonds has a higher adhesion force. It was discovered that, as expected, nodes for fresh samples had a higher adhesion force. When the tip was made of OH, the adhesion force observed in aged samples was found to be the lowest. The type of carbon nanotube used had no effect on adhesion force. However, it may have an effect on other properties of asphalt, such as elasticity, viscosity, and density. As a result, more research into this asphalt modification is required. The CART model can be directly applied to the process of developing guidelines for asphalt mix design and designing AFM experiments for asphalt because it calculates the average adhesion force for each possible combination of variables. The regression model developed for this study is shown in Equation. Throughout the development of this strategy, the ordinary least squares method was used. The coefficients' statistical significance was determined, and variables with statistically insignificant coefficients were removed from the analysis. This study looked into the effects of modifying asphalt with SB, SBS, and CNTs on its aging behavior. The nanoscale aging behavior of modified asphalt is measured by analyzing changes in its adhesive properties (SB, SBS, and CNTs). A classification and regression tree (CART) analysis was used to predict the results of the tests, which took into account a number of parameters that influence how modified asphalts behave as they age. The CART results were compared to those of the regression model. The following is a shortened version of the investigation's key findings. 
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The CART analysis reveals new explanatory relationships between the various variables influencing the behavior of oxidized asphalt at different levels of the tree. These relationships are discovered between various variables that influence oxidized asphalt behavior. The CART results were found to be more accurate (with lower MAPE and RMSE values) than the regression model results. This could be because the CART model takes into account the interaction effect, which is a significant departure from traditional regression techniques. The tip type was primarily determined by the functional group -NH3, making it the most important parameter. The use of NH3-containing compounds in the tip increases the adhesion force of the asphalt. As a result, when designing AFM experiments for asphalt adhesion, this effect must be taken into account to avoid bias in the results caused by tip type. The -NH3 tip with the SBS5 binder produced the highest mean adhesion force because this node has the highest mean adhesion force, whereas the -OH tip with aged samples produced the lowest mean adhesion force. The highest adhesion force was found when the -NH3 tip was used with the SBS5 binder because this node has the highest mean adhesion force. The type of carbon nanotube used had no effect on adhesion force. Analyzing the relationship between nanoscale adhesion and the various macrostructural changes can also lead to a rigorous conclusion for hot mix asphalt (HMA). 