A methodological approach to the formation of an expert system for ensuring the safety of landfilling during open-pit mining of mineral deposits

This article presents a methodological approach for developing an expert information-analytical system aimed at ensuring the safety of waste dump formation and operation during the development of mineral deposits. The proposed structure comprises four interrelated parts — analysis, study, forecasting, and monitoring — covering the entire cycle of processing engineering, geomechanical, and monitoring data. The approach is based on the integration of regulatory requirements, results of engineering surveys, empirical dependencies, and adaptive analytical algorithms. Particular attention is given to the logic of inference and the formation of an aggregated stability assessment, which allows for consideration of data uncertainty and justification of engineering decisions. The developed approach can serve as a foundation for a digital decision support platform in the field of mining safety and has undergone preliminary verification using pilot engineering project data.

Keywords: mine engineering structures, stability, industrial safety, expert system, intelligent data analysis, engineering surveys, parameter forecasting, monitoring, machine learning.
For citation:

Grishchenkova E. N., Kutepov Yu. I., Kutepova N. A., Vasilieva A. D. A methodological approach to the formation of an expert system for ensuring the safety of landfilling during open-pit mining of mineral deposits. MIAB. Mining Inf. Anal. Bull. 2025;(11-1):96—112. [In Russ]. DOI: 10.25018/0236_1493_2025_111_0_96.

Acknowledgements:
Issue number: 11-1
Year: 2025
Page number: 96-112
ISBN: 0236-1493
UDK: 622.83
DOI: 10.25018/0236_1493_2025_111_0_96
Article receipt date: 14.08.2025
Date of review receipt: 02.10.2025
Date of the editorial board′s decision on the article′s publishing: 10.10.2025
About authors:

Grishchenkova E. N.1, Cand. Sci. (Eng.), http://orcid.org/0000-0001-5377-7639, e-mail: grischenkova_en@pers.spmi.ru;
Kutepov Yu I.1, Dr. Sci. (Eng.), Professor, http://orcid.org/0009-0004-3333-5699, Russia, e-mail: Kutepov_YuI@pers.spmi.ru;
Kutepova N. A.1, Dr. Sci. (Eng.), Senior Researcher, http://orcid.org/0009-0006-3803-7222, e-mail: Kutepova_NA@pers.spmi.ru;
A. D. Vasilieva1, Cand. Sci. (Eng.), Researcher, e-mail: Vasileva_AD@pers.spmi.ru, ORCID ID: 0000-0002-2769-3738
1 Empress Catherine II Saint Petersburg Mining University, 199106, St. Petersburg, Vasilievsky Island, 21 Line, 2, Russia.
Conflict of interest: The authors declare that there is no conflict of interest.

For contacts:

Grishchenkova E. N., e-mail: ekgr.mail@gmail.com.

Bibliography:

1. Sidorov D.V., Ponomarenko T.V. Methodology for assessing the geodynamic state of natural and technogenic systems during the implementation of deposit development projects. Mining Journal. 2020. V. 1. pp. 49–52. DOI: 10.17580/gzh.2020.01.09.
2. Kutepov Yu. Yu. Hydrogeomechanical Justification of Pit Wall Stability during Disposal of Liquid Industrial Waste. Mining Informational and Analytical Bulletin. 2024, no. 9, pp. 65–77. [In Russ]. DOI: 10.25018/0236_1493_2024_9_0_65.
3. Pavlovich A. A., Khoreva A. Yu. Determination of Strength Characteristics of Waste Rock Mass for Slope Stability Assessment. Gornyi Zhurnal. 2023, no. 5, pp. 55–61. [In Russ]. DOI: 10.17580/gzh.2023.05.08.
4. Kalashnik N. A. Influence of Tailings Saturation on the Hydrogeomechanical State of the Containment Dam: 3D Modeling. Mining Informational and Analytical Bulletin. 2025, no. 5, pp. 144–155. [In Russ]. DOI: 10.25018/0236_1493_2025_5_0_144.
5. Zhuravkov M. A., Kologrivko A. A., Kuzmich V. A., Nikolaychik M. A. Creation of a Block Geomechanical Model of a Spent Sludge Storage Facility Using Micromine Origin & Beyond. Mining Mechanics and Mechanical Engineering. 2023, no. 1, pp. 13–22. [In Russ].
6. Chukaeva M. A., Matveeva V. A., Sverchkov I. P. Integrated Processing of High-Carbon Ash and Slag Waste. Journal of Mining Institute. 2022, vol. 253, pp. 97–104. [In Russ]. DOI: 10.31897/PMI.2022.5.
7. Bakhaeva S. P., Tur K. A., Ilyushkin V. D. Geomechanical Justification of Slope Stability in Joint Deposition of Overburden and Beneficiation Waste. Bulletin of the Kuzbass State Technical University. 2020, no. 4(140), pp. 49–59. [In Russ]. DOI: 10.26730/1999-4125-2020-4-49−59.
8. Kutepova N. A., Moseykin V. V., Kondakova V. N., Pospekhov G. B., Straupnik I. A. Engineering and Geological Properties of Coal Processing Waste in Storage Conditions. Mining Informational and Analytical Bulletin. 2022, no. 12, pp. 77–93. [In Russ]. DOI: 10.25018/0236_1493_2022_12_0_77.
9. Zhabko A. V., Volkomorova N. V., Zhabko N. M. Stability Analysis of Waste Dumps on Weak Inclined Contacts. News of the Ural State Mining University. 2021, no. 1, pp. 87–101. [In Russ]. DOI: 10.21440/2307-2091-2021-1-87−101.
10. Pashkevich M. A., Danilov A. S. Environmental Safety and Sustainable Development. Journal of Mining Institute. 2023, vol. 260, pp. 153–154. [In Russ].
11. Semyachkov A. I., Pochechun V. A., Semyachkov K. A. Hydrogeoecological Conditions of Technogenic Groundwater in Waste Disposal Facilities. Journal of Mining Institute. 2023, vol. 260, pp. 168–179. [In Russ]. DOI: 10.31897/PMI.2023.24.
12. Pashkevich M. A., Alekseenko A. V., Nureev R. R. Formation of Environmental Damage in Storage of Sulfide-Containing Beneficiation Waste. Journal of Mining Institute. 2023, vol. 260, pp. 155–167. [In Russ]. DOI: 10.31897/PMI.2023.32.
13. Shabarov A. N., Kuranov A. D. Main Directions of the Mining Industry Development under Increasingly Complex Mining and Technical Conditions. Gornyi Zhurnal. 2023, no. 5, pp. 5–34. [In Russ]. DOI: 10.17580/gzh.2023.05.01.
14. Kutepov Yu. Yu., Karasev M. A. Study and Forecast of Phosphogypsum Compaction in Dumps for Justification of Their Capacity. Gornyi Zhurnal. 2023, no. 5, pp. 61–67. [In Russ]. DOI: 10.17580/gzh.2023.05.09.
15. Sokolovskiy A. V., Gonchar N. V. Assessment of the Use of Technogenic Resources in the Development of Various Types of Mineral Raw Materials. Mining Industry. 2023, no. 5, pp. 102–107. [In Russ].
16. Makarov A.B., Livinsky I.S., Spirin V.I., Pavlovich A.A. Managing the stability of quarry slopes as a basis for responding to global challenges. News of Tula State University. Earth Sciences. 2021. V. 3. pp. 188-202. DOI: 10.46689/2218-5194-2021-3-1-182-196
17. Rylnikova M. V., Klebanov D. A., Rybin V. V., Rozanov I. Yu. Monitoring and Management of Geomechanical Condition and Stability of Mining Structures Based on Big Data Analysis. Mining Industry. 2024, no. 4, pp. 121–128. [In Russ]. DOI: 10.30686/1609-9192-2024-4-121−128.
18. Rozhdestvenskaya I. A., Zavalko N. A., Lukichev K. E., Zubenko A. V., Laffakh A. M. Application of Big Data Technologies to Increase Stability and Efficiency of the Coal Industry under Digital Transformation. Ugol. 2025, no. 1, pp. 82–92. [In Russ]. DOI: 10.18796/0041-5790-2025-1-82−92.
19. Egorov N. A., Fokin I. V., Dubinya N. V. Prediction of Stress-Strain State Behavior of Rock Samples Using Recurrent Neural Networks. Science and Technological Developments. 2024, vol. 103, no. 2, pp. 59–74. [In Russ]. DOI: 10.21455/std2024.2−4.
20. Krasyukova N. L., Panina O. V., Eremin S. G., Zubenko A. V., Laffakh A. M. Intelligent Forecasting of Ground Displacement Using Parallel Neural Network Models and High-Precision Geodetic Measurements. Mining Industry. 2025, no. 2, pp. 106–112. [In Russ]. DOI: 10.30686/1609-9192-2025-2-106−112.
21. Grigoryuk A. P., Braginskaya L. P., Seminsky I. K., Seminsky K. Zh., Kovalevsky V. V. Digital Platform for Integration and Analysis of Geophysical Monitoring Data in the Baikal Natural Territory. Electronic Libraries. 2022, vol. 25, no. 4, pp. 303–316. [In Russ]. DOI: 10.26907/1562-5419-2022-25−4-303−316.
22. Nepsha F. S., Krasilnikov M. I., Perevalov K. V. Application of a Digital Platform for Building Intelligent Power Supply Management Systems at Mining Enterprises. Automation and IT in Energy. 2021, no. 5, pp. 26–34. [In Russ].
23. Zenkov I. V., Kustikova E. A., Le Khung Ch., Silvanovich O. V., Yuronen Yu. P., Maglinets Yu. A., Raevich K. V., Gerasimova E. I., Mironova Zh. V., Skornyakova S. N. Digital Platform for Environmental Monitoring of Disturbed Lands at Open-Pit Mining Sites Using Remote Sensing and Artificial Intelligence. Ecology and Industry of Russia. 2024, vol. 28, no. 1, pp. 52–57. [In Russ]. DOI: 10.18412/1816-0395-2024-1-52−57.
24. Guman O. M., Makarov A. B., Antonova I. A., Wegner-Kozlova E. O. Digital Technologies in the Environmental Monitoring System at Solid Mineral Deposits. News of the Ural State Mining University. 2020, no. 2, pp. 97–102. [In Russ]. DOI: 10.21440/2307-2091-2020-2-97−102.
25. Rylnikov A. G., Pytalev I. A. Digital Transformation of the Mining Industry: Technical Solutions and Technological Challenges. Proceedings of the Tula State University. Earth Sciences. 2020, no. 1, pp. 470–481. [In Russ]. DOI: 10.46689/2218-5194-2020-1-1−470−481.
26. Joseph Mwanza, Peter Mashumba, Arnesh Telukdarie. A Framework for Monitoring Stability of Tailings Dams in Realtime Using Digital Twin Simulation and Machine Learning. Procedia Computer Science. 2024, vol. 232, pp. 2279–2288. DOI: 10.1016/j.procs.2024.02.047.
27. Xiaoying Zhuang, Yuhang Liu, Yuwen Hu, Hongwei Guo, Binh Huy Nguyen. Prediction of rock fracture pressure in hydraulic fracturing with interpretable machine learning and mechanical specific energy theory. Rock Mechanics Bulletin. 2025, vol. 4(14), p. 100173. DOI: 10.1016/j.rockmb.2024.10017.
28. Zhenni Li, Jiang Wang, Dong Xiao, Zhengmin Gu, Hongfei Xie. Iron ore rock classification and mine remote sensing inversion based on spectroscopy and improved extreme learning machine. Infrared Physics & Technology. 2024, vol. 140, p. 105400. DOI: 10.1016/j.infrared.2024.105400.
29. Yewuhalashet Fissha, Prashanth Ragam, Hajime Ikeda, N. Kushal Kumar, Tsuyoshi Adachi, Paul P. S., Youhei Kawamura. Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining. Rock Mechanics Bulletin. 2025, vol. 4, p. 100166. DOI: 10.1016/j.rockmb.2024.100166.
30. Botao Lin, Yan Jin, Qianwen Cao, Han Meng, Huiwen Pang, Shiming Wei. Developing a large language model for oil- and gas-related rock mechanics: Progress and challenges. Natural Gas Industry B. 2025, vol. 12, pp. 110–122. DOI: 10.1016/j.ngib.2025.03.007.
31. Jimmy Xuekai Li, Tiancheng Zhang, Yiran Zhu, Zhongwei Chen. Artificial general intelligence for the upstream geoenergy industry: A review. Gas Science and Engineering. 2024, vol. 131, p. 205469. DOI: 10.1016/j.jgsce.2024.205469.
32. Lun-Chi Chen, Mayuresh Sunil Pardeshi, Yi-Xiang Liao, Kai-Chih Pai. Application of retrieval-augmented generation for interactive industrial knowledge management via a large language model. Computer Standards & Interfaces. 2025, vol. 94, p. 1033995. DOI: 10.1016/j.csi.2025.103995.
33. Miyoung Uhm, Jaehee Kim, Seungjun Ahn, Hoyoung Jeong, Hongjo Kim. Effectiveness of retrieval augmented generation-based large language models for generating construction safety information. Automation in Construction. 2025, vol. 170, p. 105926. DOI: 10.1016/j.autcon.2024.105926.
34. Leszek Lankof, Radosław Tarkowski. GIS-based analysis of rock salt deposits’ suitability for underground hydrogen storage. International Journal of Hydrogen Energy. 2023, vol. 38, pp. 27748–27765. DOI: 10.1016/j.ijhydene.2023.03.415.
35. Arman Hazrathosseini, Ali Moradi Afrapoli. The advent of digital twins in surface mining: Its time has finally arrived. Resources Policy. 2022, vol. 80, p. 103155. DOI: 10.1016/j.resourpol.2022.103155.
36. Luke van Eyk, P. Stephan Heyns. A framework to define, design and construct digital twins in the mining industry. Computers & Industrial Engineering. 2025, vol. 200, p. 110805. 10.1016/j.cie.2024.110805.

 

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