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Mining-Induced Seismicity during Development Works in Coalbeds in the Context of Forecasts of Geomechanical Conditions
Article

An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine

1
Central Mining Institute, 1 Gwarków Sqr., 40-166 Katowice, Poland
2
School of Mining and Petroleum Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
*
Author to whom correspondence should be addressed.
Energies 2021, 14(21), 6928; https://doi.org/10.3390/en14216928
Received: 20 August 2021 / Revised: 8 October 2021 / Accepted: 18 October 2021 / Published: 21 October 2021
(This article belongs to the Special Issue Geomechanics for Energy)
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. View Full-Text
Keywords: rockburst hazard; machine learning; hard coal mine; Upper Silesian Coal Basin rockburst hazard; machine learning; hard coal mine; Upper Silesian Coal Basin
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MDPI and ACS Style

Wojtecki, Ł.; Iwaszenko, S.; Apel, D.B.; Cichy, T. An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine. Energies 2021, 14, 6928. https://doi.org/10.3390/en14216928

AMA Style

Wojtecki Ł, Iwaszenko S, Apel DB, Cichy T. An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine. Energies. 2021; 14(21):6928. https://doi.org/10.3390/en14216928

Chicago/Turabian Style

Wojtecki, Łukasz, Sebastian Iwaszenko, Derek B. Apel, and Tomasz Cichy. 2021. "An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine" Energies 14, no. 21: 6928. https://doi.org/10.3390/en14216928

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