Reconstruction of Asphalt Nanostructures via Generative Adversarial Networks

This study analyzed the influence of asphalt binder nanostructures and inherent variability on their mechanical responses. This was achieved by reconstructing asphalt binder nanostructures using generative adversarial networks (GANs). GANs are able to detect hidden patterns in a given data set automatically, and they generate distribution-free data because they are not bound to specific probability functions. The mechanical responses of GANs-generated nanostructures were analyzed using finite-element (FE) analysis. The asphalt nanostructures were captured using atomic force microscopy (AFM). To overcome the limited number of AFM nanostructures, image augmentation techniques and stochastic random fields (RF) modeling were used to generate virtual images of asphalts' nanostructures, which were utilized as the expanded training data set for the GANs. This approach helped to enhance the GANs' generalization abilities and avoid training problems such as mode collapse. Modeling parameters were optimized to expedite the training process and reduce the computational time. The results demonstrated that GANs are capable of generating probable arrangements of nanostructures, thus expanding the body of knowledge regarding probabilistic analysis of asphalt mechanical behavior. The ability of GANs to reconstruct nanostructures indicates that the complex and variable nature of asphalt nanostructures can be identified and replicated by GANs, offering the opportunity to generate many replicates of nanostructures without the need to conduct many laboratory tests. The results successfully related the properties and distribution of the asphalt nanostructures to the variation in the mechanical response.