Journal
ENERGIES
Volume 14, Issue 21, Pages -Publisher
MDPI
DOI: 10.3390/en14216928
Keywords
rockburst hazard; machine learning; hard coal mine; Upper Silesian Coal Basin
Categories
Funding
- Ministry of Science and Higher Education, Republic of Poland (Statutory Activity of the Central Mining Institute, Task [11133010]
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This article explores the use of machine learning algorithms to predict rockburst risk in underground coal mines. By utilizing various algorithms, rockburst risk prediction models were proposed. Neural network and decision tree models were found to be most effective in this aspect.
Rockburst is a dynamic rock mass failure occurring during underground mining under unfavorable stress conditions. The rockburst phenomenon concerns openings in different rocks and is generally correlated with high stress in the rock mass. As a result of rockburst, underground excavations lose their functionality, the infrastructure is damaged, and the working conditions become unsafe. Assessing rockburst hazards in underground excavations becomes particularly important with the increasing mining depth and the mining-induced stresses. Nowadays, rockburst risk prediction is based mainly on various indicators. However, some attempts have been made to apply machine learning algorithms for this purpose. For this article, we employed an extensive range of machine learning algorithms, e.g., an artificial neural network, decision tree, random forest, and gradient boosting, to estimate the rockburst risk in galleries in one of the deep hard coal mines in the Upper Silesian Coal Basin, Poland. With the use of these algorithms, we proposed rockburst risk prediction models. Neural network and decision tree models were most effective in assessing whether a rockburst occurred in an analyzed case, taking into account the average value of the recall parameter. In three randomly selected datasets, the artificial neural network models were able to identify all of the rockbursts.
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