Geologically Constrained CTGAN for Reliable Prediction of Tunnel Overbreak and Blasting Variables

Acquiring sufficient and reliable data for tunnel construction is challenging due to high costs, data scarcity, and the site-specific nature of geological conditions. This study introduces a Geologically Constrained Conditional Tabular GAN (CTGAN) framework to address these challenges by generating synthetic data that accurately reflects the geological characteristics of tunnels. Traditional approaches often overlook inherent geological variability, leading to synthetic data that lacks real-world relevance, particularly in industrial scenarios where each tunnel or its sections exhibit unique geological environments.

The proposed framework incorporates geological attributes defined by tunneling standards, including Face condition, Compressive strength, Weathering, and Crack/fissure characteristics. These attributes are categorized into levels that represent distinct geological states while maintaining consistency with practical engineering scenarios. A physical constraint module ensures logical relationships among these features, preserving the geological and physical validity of the generated data.

Designed for industrial applications, this approach enables the augmentation of limited real-world data with samples tailored to the geological characteristics of specific tunnels. It addresses data scarcity while avoiding the generation of artificially balanced samples, instead ensuring alignment with naturally occurring geological conditions. Initial results demonstrate that the constrained CTGAN effectively replicates field-observed patterns, providing a valuable tool for improving data-driven methodologies in tunnel construction and monitoring. This research highlights the importance of leveraging domain-specific constraints in generative models, contributing to reliable, context-aware data generation for geotechnical engineering applications.