Deep Learning for Geotechnical Engineering
The Effectiveness of Generative Adversarial Networks in Subsoil Schematization
F.A. Campos Montero (TU Delft - Civil Engineering & Geosciences)
Phil J. Vardon – Mentor (TU Delft - Geo-engineering)
R. Taormina – Graduation committee member (TU Delft - Sanitary Engineering)
B.E. Zuada Coelho – Graduation committee member (Deltares)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
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 pixels was created, representing a broad spectrum of stratigraphical complexity in the layered models. Each synthetic cross-section was additionally transformed into a CPT-like image with less than 1% of the original data remaining at random locations along the model. After training for 200 epochs, the best-performing SchemaGAN Generator was chosen from the validation, and the effectiveness of SchemaGAN in generating subsoil schematizations was tested against traditional interpolation methods (Nearest Neighbour, Inverse Distance Weight, Kriging, Natural Neighbour) and newer methods such as Inpainting. The evaluation metrics obtained reveal that SchemaGAN outperforms all other methods, with results characterized by clearer layer boundaries and accurate anisotropy within the layers. In contrast, Nearest Neighbour and Kriging are characterized by a lack of continuity and blurry layer boundaries respectively. Inverse Distance, Natural Neighbour and Inpainting fail to come close to the performance of the other methods. The superior performance of SchemaGAN is confirmed through a blind survey, in which SchemaGAN ranked as the top-performing method in 78% of cases according to experts in the field. Results also suggest that SchemaGAN is the least affected method by the location of CPT data along the cross-section. In a real case study, SchemaGAN demonstrates better predictive accuracy for known CPT data than both Nearest Neighbour and Kriging interpolation methods. The future potential lies in refining its performance by considering enhancements such as training with real CPT data, incorporating additional conditional inputs, and exploring larger inputs or specialized databases. All the code related to the project has been made publicly accessible.