Location
Havener Center, Miner Lounge / Wiese Atrium, 1:30pm-3:30pm
Start Date
4-2-2026 1:30 PM
End Date
4-2-2026 3:30 PM
Presentation Date
April 2, 2026; 1:30pm-3:30pm
Description
Scaling, the removal of loose rocks from excavation walls to prevent rockfalls is a critical safety procedure in underground mining operations. Approximately a quarter of fatal mining accidents are due to rockfalls. Detecting unstable rocks during scaling and assessing ground stability remain challenging due to limited visibility, dust, and environmental conditions.
Excavation disturbs the in-situ stress state, causing stress redistribution, fracture propagation, and the formation of discontinuities that lead to loose rock blocks. Ventilation airflow affects heat transfer at exposed surfaces, amplifying temperature contrasts between intact rock and fractured blocks associated with instability.
Current practices rely on manual inspection, requiring miners to operate near hazardous zones, while non-contact real-time assessment methods remain limited.
Thermal imaging captures temperature differences associated with fractures, voids, and detached rock surfaces. By integrating thermal imagery with real-time object detection models based on YOLO architectures, unstable regions can be identified rapidly and consistently under underground conditions.
Biography
Akhrorbek Narmatov is a PhD candidate in the Mining Engineering Department at Missouri University of Science and Technology, working under the supervision of Dr. Taghi Sherizadeh. He earned his master’s degree from Kyushu University, Japan, as a recipient of a highly competitive, fully funded scholarship from the Japan International Cooperation Agency (JICA). He currently serves as a Graduate Research Assistant, where his research focuses on developing AI-driven frameworks for detecting and analyzing unstable ground conditions in underground mining. His work integrates thermal imaging, LiDAR, multispectral data, and 3D photogrammetry with deep learning models to improve hazard detection, mapping, and real-time decision-making, which is supported by the National Institute for Occupational Safety and Health (NIOSH), with a focus on improving mine safety and reducing risks associated with ground instability.
Meeting Name
2026 - Miners Solving for Tomorrow Research Conference
Department(s)
Mining Engineering
Document Type
Poster
Document Version
Final Version
File Type
event
Language(s)
English
Rights
© 2026 The Authors, All rights reserved
Included in
AI-Driven Frameworks for Mitigating Rockfall Hazards, Unstable Ground Condition and Improving Safety in Underground Mine Through Thermal Imaging
Havener Center, Miner Lounge / Wiese Atrium, 1:30pm-3:30pm
Scaling, the removal of loose rocks from excavation walls to prevent rockfalls is a critical safety procedure in underground mining operations. Approximately a quarter of fatal mining accidents are due to rockfalls. Detecting unstable rocks during scaling and assessing ground stability remain challenging due to limited visibility, dust, and environmental conditions.
Excavation disturbs the in-situ stress state, causing stress redistribution, fracture propagation, and the formation of discontinuities that lead to loose rock blocks. Ventilation airflow affects heat transfer at exposed surfaces, amplifying temperature contrasts between intact rock and fractured blocks associated with instability.
Current practices rely on manual inspection, requiring miners to operate near hazardous zones, while non-contact real-time assessment methods remain limited.
Thermal imaging captures temperature differences associated with fractures, voids, and detached rock surfaces. By integrating thermal imagery with real-time object detection models based on YOLO architectures, unstable regions can be identified rapidly and consistently under underground conditions.
Comments
Advisor: Taghi Sherizadeh, sherizadeh@mst.edu