A5102828888- Superconducting Materials and Applications
- Particle Accelerators and Free-Electron Lasers
- Particle accelerators and beam dynamics
- Landslides and related hazards
- Rock Mechanics and Modeling
- Dam Engineering and Safety
- Geotechnical Engineering and Analysis
- Tunneling and Rock Mechanics
- Mineral Processing and Grinding
- Advanced Clustering Algorithms Research
- Non-Destructive Testing Techniques
- Superconductivity in MgB2 and Alloys
- Particle physics theoretical and experimental studies
- High-Velocity Impact and Material Behavior
- Particle Detector Development and Performance
- HVDC Systems and Fault Protection
- Infrastructure Maintenance and Monitoring
- Geotechnical and Geomechanical Engineering
Kunming University of Science and Technology
2023-2024
Institute of High Energy Physics
2005-2010
Chinese Academy of Sciences
2005-2008
Drift excavation induces damaged zones (EDZ) due to stress redistribution, impacting drift stability and rock deformation support. Predicting EDZ thickness is crucial, but traditional machine learning models are susceptible potential outliers in dataset. Directly eliminating outliers, however, impacts training effectiveness. This study introduces an prediction model utilising quantile loss random forest (RF) optimised by the seagull optimisation algorithm (SOA), enabling median regression...