Deep Learning for Geotechnical Engineering: The Effectiveness of Generative Adversarial Networks in Subsoil Schematization

This thesis introduces a novel Generative Adversarial Network application called SchemaGAN, which has been adapted from the Pix2Pix architecture to take Cone Penetration Test (CPT) data as a conditional input and generate subsoil schematizations. For training, validation and testing, a database of 24,000 synthetic schematizations of size 32x512...