EM - Escola de Minas
URI permanente desta comunidadehttp://www.hml.repositorio.ufop.br/handle/123456789/6
Notícias
A Escola de Minas de Ouro Preto foi fundada pelo cientista Claude Henri Gorceix e inaugurada em 12 de outubro de 1876.
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Resultados da Pesquisa
Item Avaliação das barragens de rejeito brasileiras por meio da análise de agrupamentos k médias.(2020) Paulo, Eliezer Antonio Amaral de; Pereira, Carla Maria Silva Felisberto; Santos, Tatiana Barreto dos; Oliveira, Rudinei Martins deCom a evolução tecnológica, tornou-se possível a lavra de minérios cada vez mais pobres em teor. Dessa forma, a produção de rejeitos oriundos do tratamento de minérios aumentou, levando à necessidade de ampliação das barragens em número e capacidade para armazenamento desses resíduos. Como consequência, rupturas barragens de grandes dimensões passaram a acontecer com uma frequência alarmante, como por exemplo os episódios de Brumadinho/MG em 2019 e Mariana/MG em 2015. Este artigo tem por objetivo a aplicação da técnica de estatística multivariada de agrupamento k médias para identificar as barragens de rejeito cadastradas no Cadastro Brasileiro de Barragens da Agência Nacional de Mineração que sejam semelhantes àquelas que romperam no país nos últimos anos. A técnica foi aplicada com sucesso e foram identificados seis grupos de barragens. Os grupos 1 e 2 acondicionaram as três últimas barragens de rejeito de mineração que se romperam. Foi possível notar que muitas das barragens que se encontram em estado de emergência tem características semelhantes às que se romperam. Essa informação não significa que essas barragens se encontram em situação instável, mas as mesmas devem ser avaliadas cuidadosamente.Item A methodology for the definition of geotechnical mine sectors based on multivariate cluster analysis.(2021) Nazareth, Ana Flávia Delbem Vidigal; Lana, Milene SabinoThis paper offers a new method for the definition of geotechnical sectors in open pit mines based on multivariate cluster analysis. A geologicalgeotechnical data set of a manganese open pit mine was used to demonstrate the methodology. The data set consists of a survey of geological and geotechnical parameters of the rock mass, measured directly in several points of the mine, structured initially in twenty-eight variables. After the preprocessing of the data set, the clustering technique was applied using the k-Prototype algorithm. The squared Euclidean distance was used to quantify the proximity between numerical variables, and the Jaccard’s coefficient of similarity was used to quantify the proximity between the nominal variables. The different cluster results obtained were validated by the multivariate analysis of variance. The identification of cluster structures was achieved by plotting them on the mine map for spatial visualization and definition of geotechnical sectors. These sectors are spatially contiguous and relatively homogeneous regarding their geological–geotechnical properties, indicated by a high density of points of the same group. It was possible to observe a great adherence of the proposed sectors to the mine geology, demonstrating the practical representativeness of the clustering results and the proposed sectors.Item Cluster analysis for slope geotechnical prioritization of intervention for the Estrada de Ferro Vitória-Minas.(2017) Silva, Denise de Fátima Santos da; Santos, Allan Erlikhman Medeiros; Ferreira, Bruno Trindade; Pereira, Tiago Martins; Corteletti, Rosyelle CristinaThis article proposes the geotechnical prioritization of intervention of slopes with landslide scars for the Estrada de Ferro Vitória-Minas by cluster analysis and also the proposition of a relationship between area and volume in landslide scars. Cluster definition helps the decision-making associated to containment measures, mapping and study of landslides for the Estrada de Ferro Vitória-Minas. The database is composed of the variables: slope’s height, inclination, scar area and scar volume. The distance measure used was Gower’s index, with Ward’s methods to build the clusters. Eight characteristic groups were identified. It was possible to identify stretches that need attention in relation to the propensity of landslides, such as Group 7, stretches 362+600, 093+xxxE and 419+000. Group 7 presented high values for the scarred area and volume, such as maximum area 9.75 x 104 m² and minimum area 7.49 x 104 m², and maximum volume 9.20 x 105 m³ and minimum volume 4.08 x105 m³. Group 7 presented high ranges for slope height and inclination. The set of results about Group 7 can be interpreted as stretches with a predisposition for landslides. In relation to intervention measures, Group 7 presents the sections with priority. The relationship between area and volume of landslide scars obtained by the research was compared with the relationships established in literature.