###### Abstract  
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. prediction; asphalt; adhesion; artificial neural networks; ensembles Article  
Md'IEEEexample:BSTcontrol  
However, the realistic reason for moisture causing damage is not yet construed and developed. It is suspected to be ruled in part by the physio-chemical relations between music and aggregates in the existence of moisture/water. According to [PERSON] et al. [9], a major cause of adhesion loss is due to build-up of an intermediate moisture layer which affects several mono layer thicknesses (35-45 nm) at the aggregate-asphal interface. In addition, the chemical and mineralogical composition of aggregate particles may have a more fundamental role in the creation of moisture damage than asphalt properties such as penetration grade, molecular size distribution, acid types and percentages [5]. Chemical additives such as Antistripping Agents (ASAs) are used to chemically improve the adhesion force between the asphalt and the aggregate particles to resist the water-stripping action [10]. Currently, almost all the State Departments of Transportation (DOTs) in the USA use a nominal percentage of Antistripping Agents in asphalt. 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. The test data suggested that the existing relationship between the test variables is very complicated and to some extent beyond explanation. Another study showed that the performance of ASAs is complex and non-linear in nature because of the various mix properties of asphalt [17], type and dose of ASA, and moisture condition of asphalt [12]. Hence, it is difficult to assess and predict the enhancement of resistivity of moisture damage by laboratory tests, which opens the arena for determining the best modeling technique for predicting asphalt adhesion under various conditions.