DEMIN - Departamento de Engenharia de Minas
URI permanente desta comunidadehttp://www.hml.repositorio.ufop.br/handle/123456789/510
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4 resultados
Resultados da Pesquisa
Item 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 SabinoThis 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.Item Evaluation of machine learning methods for rock mass classification.(2022) Santos, Allan Erlikhman Medeiros; Lana, Milene Sabino; Pereira, Tiago MartinsSolutions 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.Item Rock mass classification by multivariate statistical techniques and artificial intelligence.(2020) Santos, Allan Erlikhman Medeiros; Lana, Milene Sabino; Pereira, Tiago MartinsThis 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.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.