Advanced Computational Method Applications in Underground Engineering

The continuous advancement of underground engineering is crucial for large project construction and resource extraction. This progress, however, usually faces prominent challenges related to high in-situ stresses, complex geological conditions, thermo-hydro-mechanical interactions, and potential geohazards. Advanced computational methods, which encompass sophisticated numerical simulations, big data, and machine learning, are at the forefront of addressing these challenges. To achieve more accurate prediction and early-warning of rock damage and fracturing in underground engineering, as well as prevent or mitigate potential geohazards, continuous developments and applications of advanced computational methods are essential.

The goal of this research topic is to address the pressing challenges of accurately predicting rock fracturing behaviors and effectively preventing geohazards via advanced computational methods in various underground engineering scenarios. While significant progress has been made in the field study and experimental research regarding many underground scenarios, further developments and applications of advanced computational methods are also indispensable for solving complex underground engineering problems. This collection aims to promote research that advances the application of computational methods in underground engineering, thereby enhancing the predictability, safety, and efficiency of large-scale underground projects involving tunnels, deep excavations, and resource extraction. By providing a platform for the latest advancements, this collection is expected to contribute to the prediction and mitigation of risks and the continuous advancement of underground engineering practices.

This research topic invites contributions that explore the application of advanced computational methods in solving problems pertaining to underground engineering. Key themes include, but are not limited to:

• Development and application of advanced computational methods;
• Integration of big data and machine learning into advanced computational methods;
• Prediction and early-warning of rock damage and fracturing in underground engineering scenarios;
•Prevention or mitigation of geohazards in underground engineering scenarios.

Manuscripts may include original research papers, comprehensive reviews, case studies, and methodological papers.