Open stope stability assessment through artificial intelligence
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Fundação Gorceix
2020-06-01
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| Series: | REM: International Engineering Journal |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2020000300395&tlng=en |
| _version_ | 1818744904262615040 |
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| author | Allan Erlikhman Medeiros Santos Talita Káren Magalhães Amaral Guilherme Alzamora Mendonça Denise de Fátima Santos da Silva |
| author_facet | Allan Erlikhman Medeiros Santos Talita Káren Magalhães Amaral Guilherme Alzamora Mendonça Denise de Fátima Santos da Silva |
| author_sort | Allan Erlikhman Medeiros Santos |
| collection | DOAJ |
| 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%. |
| first_indexed | 2024-12-18T02:51:43Z |
| format | Article |
| id | doaj.art-09fa6cc16bc645c0b32cf02b59921e58 |
| institution | Directory Open Access Journal |
| issn | 2448-167X |
| language | English |
| last_indexed | 2024-12-18T02:51:43Z |
| publishDate | 2020-06-01 |
| publisher | Fundação Gorceix |
| record_format | Article |
| series | REM: International Engineering Journal |
| spelling | doaj.art-09fa6cc16bc645c0b32cf02b59921e582022-12-21T21:23:26ZengFundação GorceixREM: International Engineering Journal2448-167X2020-06-0173339540110.1590/0370-44672020730012Open stope stability assessment through artificial intelligenceAllan Erlikhman Medeiros Santoshttps://orcid.org/0000-0003-4302-3897Talita Káren Magalhães Amaralhttps://orcid.org/0000-0002-6859-1762Guilherme Alzamora Mendonçahttps://orcid.org/0000-0002-4516-9403Denise de Fátima Santos da Silvahttps://orcid.org/0000-0002-9695-2449Abstract 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%.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2020000300395&tlng=enopen stope stabilityartificial neural networksartificial intelligencesub level stoping |
| spellingShingle | Allan Erlikhman Medeiros Santos Talita Káren Magalhães Amaral Guilherme Alzamora Mendonça Denise de Fátima Santos da Silva Open stope stability assessment through artificial intelligence REM: International Engineering Journal open stope stability artificial neural networks artificial intelligence sub level stoping |
| title | 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_short | Open stope stability assessment through artificial intelligence |
| title_sort | open stope stability assessment through artificial intelligence |
| topic | open stope stability artificial neural networks artificial intelligence sub level stoping |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2448-167X2020000300395&tlng=en |
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