Using Machine Learning to Model Mechanical Processes in Mining: Theory, Practice, and Legal Considerations
| dc.contributor.author | Ratov, Boranbay | en |
| dc.contributor.author | Pavlychenko, Artem | en |
| dc.contributor.author | Kirin, Roman | en |
| dc.contributor.author | Pashchenko, Oleksandr | en |
| dc.contributor.author | Khomenko, Volodymyr L. | en |
| dc.contributor.author | Tileuberdi, Nurbol | en |
| dc.contributor.author | Kamyshatskyi, Oleksandr | en |
| dc.contributor.author | Sieriebriak, Stanislav | en |
| dc.contributor.author | Seidaliyev, Askar | en |
| dc.contributor.author | Muratova, Samal | en |
| dc.date.accessioned | 2025-08-29T10:38:41Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | ENG: 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. | en |
| dc.description.sponsorship | Satbayev University, Almaty, Republic of Kazakhstan; Dnipro University of Technology, Dnipro, Ukraine; State Organization «V. Mamutov Institute of Economic and Legal Research of the National Academy of Sciences of Ukraine», Kyiv, Ukraine; Volodymyr Dahl East Ukrainian National University, Kyiv, Ukraine; Yessenov University, Aktau, Republic of Kazakhstan | en |
| dc.identifier.citation | Ratov B., Pavlychenko A., Kirin R., Pashchenko O., Khomenko V., Tileuberdi N., Kamyshatskyi O., Sieriebriak S., Seidaliyev A., Muratova S. Using Machine Learning to Model Mechanical Processes in Mining: Theory, Practice, and Legal Considerations. Engineered Science. 2025. Vol. 33. Art. 1419. DOI: http://dx.doi.org/10.30919/es1419. | en |
| dc.identifier.doi | http://dx.doi.org/10.30919/es1419 | en |
| dc.identifier.issn | 2576-988X (Print) | |
| dc.identifier.issn | 2576-9898( Online) | |
| dc.identifier.uri | https://www.espublisher.com/journals/articledetails/1419 | en |
| dc.identifier.uri | https://crust.ust.edu.ua/handle/123456789/20956 | en |
| dc.language.iso | en | |
| dc.publisher | Engineered Science Publisher LLC, Knoxville, USA | en |
| dc.rights | Creative Commons Attribution 4.0 International License | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject | machine learning | en |
| dc.subject | drilling optimization | en |
| dc.subject | AI in mining | en |
| dc.subject | rock mechanics | en |
| dc.subject | predictive analytics | en |
| dc.subject | КМТОМ | uk_UA |
| dc.subject.classification | TECHNOLOGY | en |
| dc.subject.classification | TECHNOLOGY::Information technology | en |
| dc.title | Using Machine Learning to Model Mechanical Processes in Mining: Theory, Practice, and Legal Considerations | en |
| dc.type | Article | en |