Machine learning-based multi-class classification of rock mass permeability using integrated geotechnical and geophysical parameters

Integrating Kernel-Based Machine Learning Techniques with Geospatial Analysis for Landslide Susceptibility Mapping in Sikkim, India

Landslides are among the most destructive natural hazards, causing substantial loss of life and infrastructure, particularly in mountainous regions. Accurate identification of landslide-prone areas remains challenging due to complex and nonlinear interactions among geological, topographical, and environmental factors...

Interpretable machine learning for automatic rock mass classification in TBM tunneling: a case study of Beishan URL

Accurate rock mass classification is essential for safe tunnel boring machine (TBM) excavation, yet raw TBM monitoring data are often affected by shutdowns, machine adjustments, and transient disturbances, weakening their correlation with geological conditions. This study proposes an interpretable data-driven framework...

Earthquake Damage Risk Classification Using Machine Learning Models Based on Built-Up Area Indices from Satellite Imagery

Various approaches, scaling methods, and a relatively low-cost methodology are used for the initial screening of earthquake-prone areas without the need for field data collection, with the potential to apply these methods to other seismic regions.

Toward Practical Rock Mass Classification and Conversion: An Interpretable Data‑Driven Framework for Mapping Q‑System Parameters to RMR

Rock Strength Classification in a Brazilian Iron Ore Mine Using Operational Drilling Variables and Machine Learning

Rock strength is a key parameter for mine planning and operational optimization, but conventional laboratory testing is costly and provides limited spatial coverage. This study develops a methodology for classifying operational rock-strength classes in a Brazilian iron ore mine using reverse circulation (RC) drilling d...

A machine learning-assisted geophysical–geotechnical approach for improved engineering site assessment

A machine learning-assisted geophysical–geotechnical framework that integrates Electrical Resistivity Tomography, Seismic Refraction Tomography, and borehole-derived Standard Penetration Test data to improve subsurface characterization and engineering site assessment is presented.