Using Machine Learning to Model Mechanical Processes in Mining: Theory, Practice, and Legal Considerations
Ratov B. Pavlychenko A. Kirin R. Pashchenko O. Khomenko V. Tileuberdi N. Kamyshatskyi O. Sieriebriak S. Seidaliyev A. Muratova S.
February 2025Engineered Science Publisher
Engineered Science
2025#33
Artificial intelligence (AI) technologies, though critical for economic development, also pose risks of unpredictable outcomes and loss of control. Thus, a legal framework is necessary to regulate their use. International and state oversight is required to establish clear rules of conduct for all parties involved in AI relations, ensuring these technologies remain human-oriented and secure. In geological studies, AI can enhance the accuracy of predictions, such as improving the understanding of rock behavior during drilling. Machine learning methods, including linear regression and gradient boosting, have proven effective in predicting the mechanical properties of rocks, which helps optimize drilling operations and minimize risks like equipment damage. However, models must be fine-tuned to account for more complex dependencies, such as mineralogical characteristics. Despite the effectiveness of AI, challenges remain, including the need for high-quality data and the potential for overfitting in some methods. Incorporating AI studies into the geological code is crucial for effectively managing these technologies. By enhancing transparency, security, and accountability in AI systems, governments can mitigate risks while fostering innovation. In geology, AI’s potential for reducing drilling costs and improving safety, as well as its application to other areas like mining and construction, will drive significant advancements in scientific and industrial fields.
AI in mining , Drilling optimization , Machine learning , Predictive analytics , Rock mechanics
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Department of Geophysics and Seismology, Satbayev University, Almaty, 050013, Kazakhstan
Department of Ecology and Technologies of Environmental Protection, Dnipro University of Technology, Dnipro, 49005, Ukraine
State Organization «V. Mamutov Institute of Economic and Legal Research of the National Academy of Sciences of Ukraine», Kyiv, 01601, Ukraine
Oil and Gas Engineering and Drilling Department, Dnipro University of Technology, Dnipro, 49005, Ukraine
Department of Hydrogeology, Engineering and Oil and Gas Geology, Satbayev University, Almaty, 050013, Kazakhstan
Department of Material Science and Heat Treatment of Metals, Ukrainian State University of Science and Technologies, Dnipro, 49005, Ukraine
Department of Economics and Entrepreneurship, Volodymyr Dahl East Ukrainian National University, Kyiv, 93400, Ukraine
Department of Petrochemical Engineering, Yessenov University, Aktau, 130000, Kazakhstan
Department of Geophysics and Seismology
Department of Ecology and Technologies of Environmental Protection
State Organization «V. Mamutov Institute of Economic and Legal Research of the National Academy of Sciences of Ukraine»
Oil and Gas Engineering and Drilling Department
Department of Hydrogeology
Department of Material Science and Heat Treatment of Metals
Department of Economics and Entrepreneurship
Department of Petrochemical Engineering
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