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

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Łukasz Wojtecki, Sebastian Iwaszenko, Derek B. Apel, Tomasz Cichy
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	rockburst hazard machine learning hard coal mine Upper Silesian Coal Basin Technology T
Acceso en línea:	https://doaj.org/article/24be8949220a4260a5754a93c2894ec7
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:24be8949220a4260a5754a93c2894ec7
record_format	dspace
spelling	oai:doaj.org-article:24be8949220a4260a5754a93c2894ec72021-11-11T15:45:31ZAn Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine10.3390/en142169281996-1073https://doaj.org/article/24be8949220a4260a5754a93c2894ec72021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/6928https://doaj.org/toc/1996-1073Rockburst 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.Łukasz WojteckiSebastian IwaszenkoDerek B. ApelTomasz CichyMDPI AGarticlerockburst hazardmachine learninghard coal mineUpper Silesian Coal BasinTechnologyTENEnergies, Vol 14, Iss 6928, p 6928 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	rockburst hazard machine learning hard coal mine Upper Silesian Coal Basin Technology T
spellingShingle	rockburst hazard machine learning hard coal mine Upper Silesian Coal Basin Technology T Łukasz Wojtecki Sebastian Iwaszenko Derek B. Apel Tomasz Cichy An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine
description	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.
format	article
author	Łukasz Wojtecki Sebastian Iwaszenko Derek B. Apel Tomasz Cichy
author_facet	Łukasz Wojtecki Sebastian Iwaszenko Derek B. Apel Tomasz Cichy
author_sort	Łukasz Wojtecki
title	An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine
title_short	An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine
title_full	An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine
title_fullStr	An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine
title_full_unstemmed	An Attempt to Use Machine Learning Algorithms to Estimate the Rockburst Hazard in Underground Excavations of Hard Coal Mine
title_sort	attempt to use machine learning algorithms to estimate the rockburst hazard in underground excavations of hard coal mine
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/24be8949220a4260a5754a93c2894ec7
work_keys_str_mv	AT łukaszwojtecki anattempttousemachinelearningalgorithmstoestimatetherockbursthazardinundergroundexcavationsofhardcoalmine AT sebastianiwaszenko anattempttousemachinelearningalgorithmstoestimatetherockbursthazardinundergroundexcavationsofhardcoalmine AT derekbapel anattempttousemachinelearningalgorithmstoestimatetherockbursthazardinundergroundexcavationsofhardcoalmine AT tomaszcichy anattempttousemachinelearningalgorithmstoestimatetherockbursthazardinundergroundexcavationsofhardcoalmine AT łukaszwojtecki attempttousemachinelearningalgorithmstoestimatetherockbursthazardinundergroundexcavationsofhardcoalmine AT sebastianiwaszenko attempttousemachinelearningalgorithmstoestimatetherockbursthazardinundergroundexcavationsofhardcoalmine AT derekbapel attempttousemachinelearningalgorithmstoestimatetherockbursthazardinundergroundexcavationsofhardcoalmine AT tomaszcichy attempttousemachinelearningalgorithmstoestimatetherockbursthazardinundergroundexcavationsofhardcoalmine
_version_	1718434073316163584

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

Ejemplares similares