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

Agora exibindo 1 - 10 de 27
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    Three‑dimensional stability analyses and sensitivity studies of the input parameters in a global failure of an open pit slope : a case study.
    (2023) Oliveira, Davidson Paulo Azevedo; Lana, Milene Sabino
    This work aims to study the parameters afecting a global slope failure in an open pit mine. The event was a complex failure, formed by an upper layer of a weathered rock, which behaved like a soil mass, above two faults, which intersect to form a wedge. This upper soil layer has pressured the wedge leading to its failure. It occurred in the northern sector of the pit in 2013, causing the interruption of mining activity for about 1 year. The geomechanical parameters were defned from an extensive feld survey database; lab tests and Schmidt hammer tests were performed in the region of the failure. The HoekBrown criterion was adopted for the rock mass; Barton-Bandis for the discontinuities and Mohr-Coulomb for the weathered rocks. Three-dimensional limit equilibrium methods were applied using the average strength parameters. The factor of safety (F.S.) was 1.33, a situation which indicated relative stability. Thus, sensitivity analyses were performed to defne the parameters that conditioned the failure, by progressively reducing the discontinuity shear strength via percentage factors, until F.S reached 1.0. Failure surface geometry obtained in the analysis was close to the failure observed at the feld. The susceptibility condition of the wedge failure was evaluated through kinematic analyses in two scenarios; F.S = 1.33 and F.S. = 1. The results indicated a signifcant increase in the probability of kinematic feasibility when F.S. was 1. The reduction of the slope dip should ensure a stable condition. Moreover, the water level should be kept far from the slope face.
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    Quality index of dimension stones for application in building industry using technological characterization lab tests.
    (2022) Zagôto, Juliano Tessinari; Lana, Milene Sabino; Pereira, Tiago Martins
    Dimension stones have been used in many different environments, like airports, shopping malls, commercial buildings and houses, due to their beauty and quality. However, the choice of the ideal material for application in a given environment is a challenging decision. Esthetic patterns determine the choice in most of the cases, instead of the physical and mechanical properties, which can cause damages and even accidents. Thus, this research aims to propose a quality index using technological characterization lab tests established by international and national regulations. This index can be used to help practitioners in the choice of the proper dimension stone for a given environment, minimizing pathologies. An extensive database encompassing many lab tests for technological characterization of dimension stones was used in this research.
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    Machine learning applied to the prediction of rockfall slope probability.
    (2022) Silveira, Larissa Regina Costa; Lana, Milene Sabino; Santos, Tatiana Barreto dos
    The objective of this work is to propose a predictive model of rockfall slope probability in rock slopes using the KNearest Neighbors (KNN) method. A dataset composed by 220 rock slopes was used, whose variables are related to the presence of water, characteristics of the rock mass, degree of overhang, among others. For each slope of the dataset, rockfall probability (high, medium, or low) is known and determined by cluster analysis. The number of the nearest neighbors (k) ranged from 1 to 20. The obtained average accuracy of the tested predictive models was equal to 78.4%. The models produced satisfactory results in the prediction of the rockfall probability, since the area under the ROC curve was equal to 0.80. The best model was selected based on the k value with the highest accuracy and the highest area under the ROC curve. The selected model had a k value equal to 7.
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    A new methodology for rockfall hazard assessment in rocky slopes.
    (2022) Silveira, Larissa Regina Costa; Lana, Milene Sabino; Alameda Hernández, Pedro Manuel; Santos, Tatiana Barreto dos
    This article presents an approach to rockfall hazard assessment for rocky slopes based on a previously published rockfall hazard methodology. The original method is appropriate to high alpine rocky slopes exposed to large scale deformations. It evaluates the parameters related to the geomechanical characterization of rock mass, indications of activity, external influences and event intensity. The original methodology was modified to consider different contexts, including geological, climatic and social environments. Parameters related to external influences were modified; the geometry and characteristics of the slope and the catchment area were introduced. The original methodology and the new proposal were applied to two urban slopes and one railway slope in order to test and compare the methods. The original proposal could not represent the rockfall conditions of these slopes. The new proposal was validated using two mine slopes, whose conditions of stability are known. The results of the analyses with the urban slope and the railway slope were coherent with the situation observed at the field. The validation in the mine slopes showed that this approach is applicable in several situations, being able to determine how hazardous a slope is in relation to rockfall events.
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    Evaluation of machine learning methods for rock mass classification.
    (2022) Santos, Allan Erlikhman Medeiros; Lana, Milene Sabino; Pereira, Tiago Martins
    Solutions in geotechnics have been optimizing with the aid of machine learning methods. The aim of this paper is to apply different machine learning algorithms in order to achieve rock mass classification. It is demonstrated that RMR classifi- cation system can be obtained using only variables which are closely related to rock mass quality, instead of all RMR variables, without missing significant accuracy. The different machine learning algorithms used are the naı ̈ve Bayes, random forest, artificial neural networks and support vector machines. The variables to calculate RMR, selected by factor analysis, are: rock strength, rock weathering, spacing, persistence and aperture of discontinuities and presence of water. The machine learning models were trained and tested thirty times, with random subsampling, using two-thirds of the total database for training sample. The models presented average accuracy greater than 0.81, which was calculated from the confusion matrix, using the proportion of true positives and true negatives in the test sample. Significant values of efficiency, precision and reproducibility rates were achieved. The study shows the application of machine learning algorithms allows obtaining the RMR classes, even with a small number of variables. In addition, the results of the evaluation metrics of the developed algorithms show that the methodology can be applied to new database, working as a valuable way to achieve rock mass classification.
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    Probability density function of rock mass discontinuity distances.
    (2020) Bruzzi, Antonio Fernando Gorgulho; Alameda Hernández, Pedro Manuel; Klen, André Monteiro; Pereira, Tiago Martins; Lana, Milene Sabino
    Rock masses are presenting an enduring challenge since the first authors began trying to model these natural structures. This modelling needs a parametrisation and a latter value assignment for those parameter value series, which show high variability, and a lack of clear patterns, in nature. Understanding the statistical nature of these values is an essential goal of rock mechanics, searching for the most appropriate probability density function (pdf) for fitting each one of those parameter values. The identification of those pdf is an aid for a better understanding of the rock mass nature and a need for correct discrete fracture network generation, or any probabilistic calculation. The parameter studied in this work is the degree of fracturing, in particular: the distances between fractures in a borehole rock core, which determines the Rock Quality Designation (RQD) value. The most accepted hypothesis is that these values fit a negative exponential pdf, however, with some criticism. Through the analysis of 1985 m of borehole rock core from five lithologies from two rock masses, this work shows the lognormality of the data series. The negative exponential does not fit correctly; however, the old approach for RQD based on this hypothesis offers satisfying results. Furthermore, it has been observed that schist presents the unclearer random pattern.
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    Rock mass classification by multivariate statistical techniques and artificial intelligence.
    (2020) Santos, Allan Erlikhman Medeiros; Lana, Milene Sabino; Pereira, Tiago Martins
    This study aims to improve the quality and accuracy of RMR classification system for rock masses in open pit mines. A database of open pit mines comprising basic parameters for obtaining the RMR was used. Techniques applied in this research were multivariate statistics and artificial intelligence. In relation to multivariate statistics, factor analysis was capable of identifying underlying factors not observable in the original variables, using the variables of these factors in the classification system, instead of all RMR variables. The proposed classifier was obtained by training neural networks. The results of the factor analysis allowed the identification of three common factors. Factor 1 represents the strength and weathering of the rock mass. Factor 3 represents the fracturing degree of the rock mass. Finally Factor 2 represents water flow conditions. Thirty artificial neural networks were trained with randomly selected training samples. The trained networks proved to be effective and stable. Regarding the validation of the networks, the values obtained for the overall probability of success and apparent error rate showed normal distributions and a low dispersion rate, with average rates of 0.87 and 0.13, respectively. Regarding specific errors, error values were recorded only between contiguous RMR classes. The major contribution of the study is to present a new methodology for achieving rock mass classifications based on mathematical and statistical fundamentals, aiming at optimising the selection of variables and consequent reduction of subjectivity in the parameters and classification methods.
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    A methodology for the definition of geotechnical mine sectors based on multivariate cluster analysis.
    (2021) Nazareth, Ana Flávia Delbem Vidigal; Lana, Milene Sabino
    This 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.
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    Influence of induced stresses by sublevel stopes in stability conditions of development openings in underground mines.
    (2016) Mendonça, Guilherme Alzamora; Lana, Milene Sabino; Figueiredo, Rodrigo Peluci de
    This work aims to investigate the influence of induced stresses by sublevel stopes in development excavations, which are excavated to access these stopes. Parametric studies changing the position of development openings in relation to stopes were performed in order to evaluate the stability conditions of these openings. Numerical modeling using finite element method was applied to the simulations. An elastic behavior of the rock mass was assumed to allow the simulation of a lot of different opening locations. The results have showed distinct scenarios. Some cases of global collapse were found as well as some situations where the integrity of the openings could be kept. Therefore, the most favorable situations were chosen to perform a plastic analysis in order to have a better knowledge of opening stability conditions. The geometry of the excavations from Caraiba Mining Company, which extracts copper from an underground mine in Brazil, was used in these analyses to illustrate a real situation where many failure problems in these development openings were observed.
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    Quantitative hazard assessment system (Has-Q) for open pit mine slopes.
    (2018) Santos, Tatiana Barreto dos; Lana, Milene Sabino; Pereira, Tiago Martins; Canbulat, Ismet
    Rock slope hazard assessment is an important part of risk analysis for open pit mines. The main parameters that can lead to rock slope failures are the parameters traditionally used in geomechanical classifications, the slope geometrical parameters and external factors like rainfall and blasting. This paper presents a methodology for a hazard assessment system for open pit mine slopes based on 88 cases collated around the world using principal components analysis, discriminant analysis and confidence ellipses. The historical cases used in this study included copper, gold, iron, diamond, lead and zinc, platinum and claystone mines. The variables used in the assessment methodology are uniaxial compressive strength of intact rock; spacing, persistence, opening, roughness, infilling and orientation of the main discontinuity set; weathering of the rock mass; groundwater; blasting method; and height and inclination of the pit. While principal component analysis was used to quantify the data, the discriminant analysis was used to establish a rule to classify new slopes about its stability condition. To provide a practical hazard assessment system, confidence ellipses were used to propose a hazard graph and generate the HAS-Q. The discriminant rule developed in this research has a high discrimination capacity with an error rate of 11.36%.