Open stope stability assessment through artificial intelligence

Detalhes bibliográficos
Autor(a) principal: Santos,Allan Erlikhman Medeiros
Data de Publicação: 2020
Outros Autores: Amaral,Talita Káren Magalhães, Mendonça,Guilherme Alzamora, Silva,Denise de Fátima Santos da
Tipo de documento: Artigo
Idioma: eng
Título da fonte: REM - International Engineering Journal
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2020000300395
Resumo: Abstract Underground mining is a set of methods that allows the extraction of ore in depth, ensuring sustainability and economic viability. One of the problems that arise in underground mine operations is open stope stability. The method for assessing stabil ity of open stopes is the stability graph proposed by Mathews et al. (1981). It is possible to estimate and provide information about this stability and assist in the decision mak ing about its viability. With the data obtained from 35 open stopes from a Zinc mine, the present study aims to use artificial intelligence techniques, specifically artificial neural networks, to process the data and classify the open stopes according to the sta bility regions of the graph. As a result, the applied methodology presented good asser tiveness for the classification of two classes, stable and unstable open stopes, resulting in a global probability success of 82% overall hit probability and 18% apparent error rate. For the classification into three classes, adding the transitional open stopes, the internal validation presented a global probability success of 91% and apparent error rate of 9%. In external validation, the network evaluation measures presented values of global probability success of 42% and apparent error rate of 58%.
id FG-1_4cc7744a217978be4b46d25916bfa10c
oai_identifier_str oai:scielo:S2448-167X2020000300395
network_acronym_str FG-1
network_name_str REM - International Engineering Journal
repository_id_str
spelling Open stope stability assessment through artificial intelligenceopen stope stabilityartificial neural networksartificial intelligencesub level stopingAbstract Underground mining is a set of methods that allows the extraction of ore in depth, ensuring sustainability and economic viability. One of the problems that arise in underground mine operations is open stope stability. The method for assessing stabil ity of open stopes is the stability graph proposed by Mathews et al. (1981). It is possible to estimate and provide information about this stability and assist in the decision mak ing about its viability. With the data obtained from 35 open stopes from a Zinc mine, the present study aims to use artificial intelligence techniques, specifically artificial neural networks, to process the data and classify the open stopes according to the sta bility regions of the graph. As a result, the applied methodology presented good asser tiveness for the classification of two classes, stable and unstable open stopes, resulting in a global probability success of 82% overall hit probability and 18% apparent error rate. For the classification into three classes, adding the transitional open stopes, the internal validation presented a global probability success of 91% and apparent error rate of 9%. In external validation, the network evaluation measures presented values of global probability success of 42% and apparent error rate of 58%.Fundação Gorceix2020-09-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2020000300395REM - International Engineering Journal v.73 n.3 2020reponame:REM - International Engineering Journalinstname:Fundação Gorceix (FG)instacron:FG10.1590/0370-44672020730012info:eu-repo/semantics/openAccessSantos,Allan Erlikhman MedeirosAmaral,Talita Káren MagalhãesMendonça,Guilherme AlzamoraSilva,Denise de Fátima Santos daeng2020-06-17T00:00:00Zoai:scielo:S2448-167X2020000300395Revistahttps://www.rem.com.br/?lang=pt-brPRIhttps://old.scielo.br/oai/scielo-oai.php||editor@rem.com.br2448-167X2448-167Xopendoar:2020-06-17T00:00REM - International Engineering Journal - Fundação Gorceix (FG)false
dc.title.none.fl_str_mv Open stope stability assessment through artificial intelligence
title Open stope stability assessment through artificial intelligence
spellingShingle Open stope stability assessment through artificial intelligence
Santos,Allan Erlikhman Medeiros
open stope stability
artificial neural networks
artificial intelligence
sub level stoping
title_short Open stope stability assessment through artificial intelligence
title_full Open stope stability assessment through artificial intelligence
title_fullStr Open stope stability assessment through artificial intelligence
title_full_unstemmed Open stope stability assessment through artificial intelligence
title_sort Open stope stability assessment through artificial intelligence
author Santos,Allan Erlikhman Medeiros
author_facet Santos,Allan Erlikhman Medeiros
Amaral,Talita Káren Magalhães
Mendonça,Guilherme Alzamora
Silva,Denise de Fátima Santos da
author_role author
author2 Amaral,Talita Káren Magalhães
Mendonça,Guilherme Alzamora
Silva,Denise de Fátima Santos da
author2_role author
author
author
dc.contributor.author.fl_str_mv Santos,Allan Erlikhman Medeiros
Amaral,Talita Káren Magalhães
Mendonça,Guilherme Alzamora
Silva,Denise de Fátima Santos da
dc.subject.por.fl_str_mv open stope stability
artificial neural networks
artificial intelligence
sub level stoping
topic open stope stability
artificial neural networks
artificial intelligence
sub level stoping
description Abstract Underground mining is a set of methods that allows the extraction of ore in depth, ensuring sustainability and economic viability. One of the problems that arise in underground mine operations is open stope stability. The method for assessing stabil ity of open stopes is the stability graph proposed by Mathews et al. (1981). It is possible to estimate and provide information about this stability and assist in the decision mak ing about its viability. With the data obtained from 35 open stopes from a Zinc mine, the present study aims to use artificial intelligence techniques, specifically artificial neural networks, to process the data and classify the open stopes according to the sta bility regions of the graph. As a result, the applied methodology presented good asser tiveness for the classification of two classes, stable and unstable open stopes, resulting in a global probability success of 82% overall hit probability and 18% apparent error rate. For the classification into three classes, adding the transitional open stopes, the internal validation presented a global probability success of 91% and apparent error rate of 9%. In external validation, the network evaluation measures presented values of global probability success of 42% and apparent error rate of 58%.
publishDate 2020
dc.date.none.fl_str_mv 2020-09-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2020000300395
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2020000300395
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0370-44672020730012
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Fundação Gorceix
publisher.none.fl_str_mv Fundação Gorceix
dc.source.none.fl_str_mv REM - International Engineering Journal v.73 n.3 2020
reponame:REM - International Engineering Journal
instname:Fundação Gorceix (FG)
instacron:FG
instname_str Fundação Gorceix (FG)
instacron_str FG
institution FG
reponame_str REM - International Engineering Journal
collection REM - International Engineering Journal
repository.name.fl_str_mv REM - International Engineering Journal - Fundação Gorceix (FG)
repository.mail.fl_str_mv ||editor@rem.com.br
_version_ 1754734691507765248