Research progress and prospect of intelligent prediction and disaster risk assessment of open-pit mining surface deformation
Research on the prediction and disaster risk assessment of surface deformation in open-pit mines serves to improve the accuracy of disaster warning and make safety control decisions. In recent years, the development of big data, cloud computing and artificial intelligence methods has provided techni...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Emergency Management Press
2024-12-01
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| Series: | 矿业科学学报 |
| Subjects: | |
| Online Access: | http://kykxxb.cumtb.edu.cn/en/article/doi/10.19606/j.cnki.jmst.2024904 |
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| Summary: | Research on the prediction and disaster risk assessment of surface deformation in open-pit mines serves to improve the accuracy of disaster warning and make safety control decisions. In recent years, the development of big data, cloud computing and artificial intelligence methods has provided technical support for the intelligent transformation of traditional mines. This paper summarizes the research progress of surface deformation hazards in open-pit mines from three aspects: intelligent perception, intelligent prediction and disaster risk evaluation of surface deformation. Specifically, by reviewing the intelligent monitoring technologies of mine surface deformation, this study indicates that the choice of intelligent monitoring methods should factor in data accuracy, installation cost and post-processing speed, reviews the intelligent modeling methods of surface deformation prediction regarding the methodological combination of traditional deformation prediction and intelligent optimization, machine learning and deep learning, and summarizes the mechanism behind the typical risk assessment method of mine deformation hazards. Based on the current research progress, we discuss the existing research gap and prospects of intelligent prediction and disaster risk assessment of surface deformation in open-pit mines so as to offer reference to the intelligent upgrading of mine disaster prevention and control. |
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| ISSN: | 2096-2193 |