Open stope stability assessment through artificial intelligence
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , |
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%. |
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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 |