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.

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 stability 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 making 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 stability regions of the graph. As a result, the applied methodology presented good assertiveness 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%.

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 10 4 m², and maximum volume 9.20 x 10 5 m³ and minimum volume 4.08 x10 5 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.

Landslides have been the object of extensive studies in the world, not only for their importance as active agents of modifications of relief forms, but also because can damages and losses to people and exposed structures, affecting various kinds of enterprises. This study had as objective the determination of influencing parameters on the development of landslides in the slopes aside of Estrada de Ferro Vitória-Minas (EFVM). EFVM is located in the southeastern region in Brazil and is an important railroad for the transportation of iron ore to the steel mills and for exportation, as well as for passenger transportation. The database used herein was collected from field work in EFVM, together with image processing and data in laboratory tests. The parameters selected to be evaluated were Atterberg limits, cohesion, friction angle, permeability and classification of soil in the slopes. Estimates were done on the volumes and areas of landslides that have already occurred in the slopes. Among the studied parameters, the results obtained for the Atteberg limits and soil cohesion were the most relevantly correlated with the field results, which is in accordance with other studies from literature. It is concluded that Atterberg limits are directly related to soil ruptures, and soil cohesion contributes to soil stabilization in slopes.

A avaliação geológico-geotécnica de taludes rodoviários são de extrema importância para a segurança da via, pois a partir destes estudos é possível elaborar programas de prevenção e de alerta e a implantação de obras para redução dos danos nestes empreendimentos. Assim, este trabalho teve como objetivo a caracterização geológico-geotécnica e avaliação do perigo à queda de blocos dos taludes localizados às margens da rodovia BR-262, entre as cidades de Betim e Nova Serrana, em Minas Gerais. Em relação a classificação dos maciços foram aplicados os sistemas RMR, Sistema-Q e GSI. Posteriormente, foram realizadas, análise cinemática, análise de estabilidade e por fim taludes foram hierarquizados conforme o perigo à queda de blocos. Em nenhum dos taludes o RMR foi inferior à regular. Os resultados das demais classificações foram concordantes com o RMR. Os fatores de segurança indicaram que os taludes estão estáveis. O talude com maior grau de perigo foi o Talude P4 - A e o de menor perigo foi o Talude PM1.