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Assessment Of Antistripping Agents On Adhesion Of Damaged Asphalt By Neural Network, Sanjida Ahsan

Civil Engineering ETDs

In this study, Neural Network (NN) model is developed to quantify nano-level adhesion force of moisture damaged asphalt binder using Atomic Force Microscopy (AFM) test data. AFM data contains five point force-distance values determined for some specific asphalt chemical functional groups. Asphalt binder samples contain different types and percentages of polymer modifiers and antistripping agents (ASA). Due to complex and nonlinear interaction between the asphalt properties and adhesion force of asphalt, it is difficult to assess the effects of asphalt binder properties on the adhesion forces using laboratory AFM testing. NN has the ability to recognize and trace the complex …