Establishment And Extension Of Digital Aggregate Database Using Auxiliary Classifier Wasserstein Gan With Gradient Penalty

For road construction, the morphological characteristics of coarse aggregates such as angularity and sphericity have a considerable influence on asphalt pavement performance. In traditional aggregate simulation processes, images of real coarse grains are captured, and their parameters are extracted manually for reproducing them in a numerical simulation such as Discrete Element Modeling (DEM). Generative Adversarial Networks can generate aggregate images, which can be stored in the Aggregate DEM Database directly. In this paper, it has been demonstrated that applying Auxiliary Classifier Wasserstein GANs with gradient penalty (ACWGAN-gp) is reliable and efficient for the establishment of an aggregate image database. In addition, the distribution of original images was compared with that of images generated based on ACGAN and ACWGAN-gp models. Generated images were validated through obtaining identifiable edge coordinates and represented as DEM input in the simulation process. The results prove that the ACWGAN-gp approach can be used for generating aggregate images for the DEM database. It successfully generates high-quality images of aggregates with a representative distribution of morphologies used for DEM simulation. This work shows convenience and efficiency for machine learning applications in the road construction field.