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All Journal International Journal of Electrical and Computer Engineering Civil Engineering Journal
Tomilov, Aleksandr
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering

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Advanced Digital Modeling of Stress–Strain Behavior in Rock Masses to Ensure Stability of Underground Mine Workings Demin, Vladimir; Kalinin, Alexey; Tomilova, Nadezhda; Tomilov, Aleksandr; Akpanbayeva, Assem; Shokarev, Denis; Popov, Anton
Civil Engineering Journal Vol 11, No 3 (2025): March
Publisher : Salehan Institute of Higher Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/CEJ-2025-011-03-014

Abstract

This study focuses on optimizing underground support systems through advanced numerical modeling and geomechanical assessment. The research aims to refine reinforcement parameters for underground mine workings by analyzing the stress-strain behavior of rock masses using Rocscience RS2 software. The study integrates geological and geotechnical data, including field observations and numerical simulations, to enhance the accuracy of support system designs. The methodology is based on the finite element method (FEM) and the Hoek–Brown softening model, allowing the identification of plastic deformation zones and stress redistribution patterns. The results confirm that maximum stress increases by 35–40% for every 100 m of depth, necessitating enhanced reinforcement. The study evaluates hybrid support systems, specifically steel-polymer bolts with shotcrete, demonstrating a 15% reduction in plastic deformations compared to conventional methods. The findings highlight the importance of continuous geotechnical monitoring and adaptive reinforcement strategies to ensure stability in highly fractured rock masses. The proposed approach provides a more precise prediction of excavation stability, contributing to the development of safer and more efficient underground mining practices. Future research may include the integration of intelligent monitoring systems equipped with real-time sensors to further optimize support strategies and long-term stability assessments. Doi: 10.28991/CEJ-2025-011-03-014 Full Text: PDF
Application of artificial intelligence and machine learning in expert systems for the mining industry: modern methods and technologies Mutovina, Natalya; Nurtay, Margulan; Kalinin, Alexey; Tomilov, Aleksandr; Tomilova, Nadezhda
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3291-3308

Abstract

The mining industry has changed significantly in recent decades with the introduction of advanced technologies such as artificial intelligence (AI) and machine learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the mining industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the mining industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.
Co-Authors Akpanbayeva, Assem Demin, Vladimir Kalinin, Alexey Mutovina, Natalya Nurtay, Margulan Popov, Anton Shokarev, Denis Tomilova, Nadezhda