Applicability of geomechanical classifications for estimation of strength properties in Brazilian rock masses.

Many authors have been proposed several correlation equations between geomechanical classifications and strength parameters. However, these correlation equations have been based in rock masses with different characteristics when compared to Brazilian rock masses. This paper aims to study the applicability of the geomechanical classifications to obtain strength parameters of three Brazilian rock masses. Four classification systems have been used; the Rock Mass Rating (RMR), the Rock Mass Quality (Q), the Geological Strength Index (GSI) and the Rock Mass Index (RMi). A strong rock mass and two soft rock masses with different degrees of weathering located in the cities of Ouro Preto and Mariana, Brazil; were selected for the study. Correlation equations were used to estimate the strength properties of these rock masses. However, such correlations do not always provide compatible results with the rock mass behavior. For the calibration of the strength values obtained through the use of classification systems, stability analyses of failures in these rock masses have been done. After calibration of these parameters, the applicability of the various correlation equations found in the literature have been discussed. According to the results presented in this paper, some of these equations are not suitable for the studied rock masses

Evaluation of rock slope stability conditions through discriminant analysis.

A methodology to predict the stability status of mine rock slopes is proposed. Two techniques of multivariate statistics are used: principal component analysis and discriminant analysis. Firstly, principal component analysis was applied in order to change the original qualitative variables into quantitative ones, as well as to reduce data dimensionality. Then, a boosting procedure was used to optimize the resulting function by the application of discriminant analysis in the principal components. In this research two analyses were performed. In the first analysis two conditions of slope stability were considered: stable and unstable. In the second analysis three conditions of slope stability were considered: stable, overall failure and failure in set of benches. A comprehensive geotechnical database consisting of 18 variables measured in 84 pit-walls all over the world was used to validate the methodology. The discriminant function was validated by two different procedures, internal and external validations. Internal validation presented an overall probability of success of 94.73% in the first analysis and 68.42% in the second analysis. In the second analysis the main source of errors was due to failure in set of benches. In external validation, the discriminant function was able to classify all slopes correctly, in analysis with two conditions of slope stability. In the external validation in the analysis with three conditions of slope stability, the discriminant function was able to classify six slopes correctly of a total of nine slopes. The proposed methodology provides a powerful tool for rock slope hazard assessment in open-pit mines

Predição da condição de estabilidade de taludes de mina por meio de estatística multivariada.

Programa de Pós-Graduação em Engenharia Mineral. Departamento de Engenharia de Minas. Escola de Minas, Universidade Federal de Ouro Preto.Na geociências, as técnicas multivariadas encontram amplo campo de aplicação, tais como as técnicas de agrupamentos, analise fatorial, componentes principais, regressão multivariada, análise discriminante, entre outras (Andriotti, 1997). Neste contexto o presente trabalho propõe a aplicação de técnicas multivariadas a um banco de dados geotécnico, com objetivo de predizer as condições de estabilidade de taludes de mina. O objetivo da pesquisa é aplicar técnicas de estatística multivariada a um banco de dados geotécnico com o intuito de discriminar os taludes de mina quanto às suas condições de estabilidade. Foi utilizado o banco de dados geotécnico proposto por Naghadehi et al. (2013), que apresenta 84 taludes coletados em diferentes locais do mundo com 18 variáveis relacionadas a parâmetros geomecânicos e características dos taludes, e uma variável qualitativa relacionada às condições de estabilidade dos taludes. A metodologia propõe primeiramente a aplicação da análise de componentes principais e em seguida a aplicação do procedimento boosting nas componentes principais geradas pela análise de componentes principais, utilizando como classificador base a função discriminante linear de Fisher. O classificador, baseado na análise discriminante, gerado pela metodologia foi validado apresentando probabilidade global de acerto de 94,73% e consequentemente uma taxa de erro aparente de 5,26%, sendo que o erro relacionado aos casos em que taludes instáveis são classificados como estáveis, apresentou valores desprezíveis e o erro relacionado aos casos em que taludes estáveis sejam classificados como instáveis, apresentou uma taxa de 10,0%. No contexto do trabalho as técnicas estatísticas multivariadas forneceram uma ferramenta simples e com grande aplicabilidade para as operações de mineração relacionadas à estabilidade de taludes.In geosciences, multivariate analysis techniques find wide application, such as the cluster analysis, factor analysis, principal component analysis, multivariate regression analysis, discriminant analysis, and others (Andriotti, 1997). In context, this work aims the application of some multivariate techniques to a geotechnical database, in order to predict the mine slope stability conditions. The aim of the research is to apply multivariate statistical techniques to a geotechnical database with the intention of discriminate the mine slope in relation to their conditions of stability. It was used geotechnical database proposed by Naghadehi et al. (2013), which features 84 slopes collected in different places in the world with 18 variables related to geomechanical parameters and characteristics of the slopes, and a qualitative variable related to the conditions of stability of slopes. The methodology proposes the application of principal component analysis and then the application of the procedure boosting in principal components generated by principal component analysis, using as a base classifier the function of Fisher’s linear discriminant analysis. The discriminant function generated by the methodology was validated presenting global probability of success of 94,73% and thus an apparent error rate of 5,26%, and the error related to cases where unstable slopes are classified as stable, showed insignificant values and the error related to cases where stable slopes are classified as unstable, showed a rate of 10,0%. In the work the multivariate statistical techniques have provided a simple and very applicable to the mining operations related to slope stability tool

Assessment of mine slopes stability conditions using a decision tree approach

Abstract Continuous assessment of slope stability is important to the open pit design and operation. This article aims to present a tool for evaluating the stability conditions of rock slopes in mining, based on a global geotechnical database, using machine learning techniques. Different models are evaluated in this research: the general model, which uses all variables; the mathematical model, which uses only variables selected by the random forest (out-of-bag); and two expert-based models: the Q-Slope model and the Santos model. The validation of the model was done through the test sample, using partition confusion matrices aiming at reproducibility of the results. A study of the types of errors was carried out using Principal Component Analysis (PCA). The study of errors allowed the identification of samples that were inconsistent with the others. Afterwards, the models were redone and compared with the previous ones. The best performers are presented and discussed. The proposed methodology does not replace the classic analysis of slope stability. On the contrary, it contributes to engineers and geologists with a tool for monitoring the stability conditions of slopes in a mining operation. Slope stability analysis must be carried out throughout the mine's lifetime and, therefore, it is believed that the tool proposed here can optimize the selection of slopes most susceptible to instability

Cluster analysis for slope geotechnical prioritization of intervention for the Estrada de Ferro Vitória-Minas

Abstract This 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

Cluster analysis for slope geotechnical prioritization of intervention for the Estrada de Ferro Vit?ria-Minas.

This article proposes the geotechnical prioritization of intervention of slopes with landslide scars for the Estrada de Ferro Vit?ria-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 Vit?ria-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 m? and minimum area 7.49 x 104 m?, and maximum volume 9.20 x 105 m? and minimum volume 4.08 x105 m?. 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

Evaluation of rock slope stability conditions through discriminant analysis.

A methodology to predict the stability status of mine rock slopes is proposed. Two techniques of multivariate statistics are used: principal component analysis and discriminant analysis. Firstly, principal component analysis was applied in order to change the original qualitative variables into quantitative ones, as well as to reduce data dimensionality. Then, a boosting procedure was used to optimize the resulting function by the application of discriminant analysis in the principal components. In this research two analyses were performed. In the first analysis two conditions of slope stability were considered: stable and unstable. In the second analysis three conditions of slope stability were considered: stable, overall failure and failure in set of benches. A comprehensive geotechnical database consisting of 18 variables measured in 84 pit-walls all over the world was used to validate the methodology. The discriminant function was validated by two different procedures, internal and external validations. Internal validation presented an overall probability of success of 94.73% in the first analysis and 68.42% in the second analysis. In the second analysis the main source of errors was due to failure in set of benches. In external validation, the discriminant function was able to classify all slopes correctly, in analysis with two conditions of slope stability. In the external validation in the analysis with three conditions of slope stability, the discriminant function was able to classify six slopes correctly of a total of nine slopes. The proposed methodology provides a powerful tool for rock slope hazard assessment in open-pit mines