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

Łukasz Wojtecki, Sebastian Iwaszenko, Derek B. Apel and Tomasz Cichy
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Łukasz Wojtecki: Central Mining Institute, 1 Gwarków Sqr., 40-166 Katowice, Poland
Sebastian Iwaszenko: Central Mining Institute, 1 Gwarków Sqr., 40-166 Katowice, Poland
Derek B. Apel: School of Mining and Petroleum Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Tomasz Cichy: Central Mining Institute, 1 Gwarków Sqr., 40-166 Katowice, Poland

Energies, 2021, vol. 14, issue 21, 1-18

Abstract: 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.

Keywords: rockburst hazard; machine learning; hard coal mine; Upper Silesian Coal Basin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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