###### Abstract
Machine learning (ML) is a field of modeling which has become popular due to its adaptability and robustness. Techniques which come under the discipline of ML deal with the modeling of different aspects of given datasets without assuming any prior information about them [18]. ML techniques have been adopted in various fields due to their ability to capture complex non-linear structures and relationships [19]. Artificial Neural Networks (ANNs) are one of the ML techniques which have been preferred for prediction problems due to the fact that they provide a convenient method to map the relationship between input and output parameters. Researchers have used them as a standalone method, as well as with integration with other ML techniques, such as fuzzy logic [20]. Artificial Neural Networks (ANNs) have lately become important and alternative analytic tools to conventional methods for modeling complex and non-linear relationships [21] and can be used as an instrument for the development of models to predict the effect of ASAs. The ANN tools and approaches have been used with great success to model structural damage detection [22], material behavior modeling [23], and structural optimization [24]. [PERSON] [25] used an ANN to predict deflections of a deep beam at mid span with high level of confidence as compared to other available methods. This approach has also been used to model the nitrogen dioxide dispersion from vehicular types of exhaust emissions [26], and to simulate pollutant dispersion [27]. In the context of predicting characteristics of asphalt, few studies have been found in which ANNs have been used for this purpose, including Shafabakhsh [28] and [PERSON] [29]. However, these studies do not cover predicting the adhesion force of asphalt modified with Carbon Nanotubes (CNT). Moreover, modeling adhesion force obtained from Atomic Force Microscopy (AFM) has also not been carried out in the current literature. With this motivation, this paper attempts to evaluate the performance of the two most popular types of ANNs. Another novelty of the current research is the attempt to increase the practical convenience and accuracy of the models by employing hierarchical modeling and ensemble learning approaches on the ANNs. AFM laboratory test data were used to assess and predict the performance of different ASAs on adhesion and cohesion behavior for varying properties and moisture condition of asphalt. The literature does not agree on the use of any particular type of ANN for prediction problems. Hence, two common types of ANNs were used for the development of models in this study and their prediction performance was compared to investigate their suitability for the available dataset. This was necessary since there are no studies found on the use of ANNs for predicting adhesion of modified asphalt samples. The specific objective of this study includes developing and comparing hierarchical ANN models for prediction of the adhesion force of modified asphalt, with different polymer and ASA contents, under moisture-damaged and aged conditions. Moreover, performance of single models is also compared with ensembles which are developed using simple average methods. Materials used and modeling approach applied in this study are described in Section 2. Results are discussed in Section 3. Section 4 concludes the paper.