Sensitivity slope stability analysis is extensively used by the geotechnical engineers to access the stability of mine rock slopes. This type of analysis consists of performing stability analyses through parameter variation with the objective of determining their influence on the factor of safety. Besides the use of sensitivity analysis to access the slope stability conditions, multivariate statistics have been proved effectiveness to predict stability conditions and hazard of slopes. Sensitivity analyses and the application of a quantitative hazard system based on multivariate statistical techniques were carried out in an overall slope from an iron mine located in Minas Gerais state, Brazil. Sensitivity analyses provided a factor of safety around 1.2 and the quantitative hazard system classified the overall slope as high hazard. Then, both methods of analysis indicate the instability condition of the slope and urgent mitigating measures in the overall slope are suggested.
Traditionally, slope stability analysis is carried out using limit equilibrium methods. The result of this type of analysis is expressed by the Factor of Safety (FS). It is defined by the ratio between the resistance forces and the driving forces. In order to increase the reliability of slope stability analysis, sensitivity analyses with variation of rock mass strength and geometrical parameters can improve significantly the confidence in factor of safety results.
Besides the use of sensitivity analysis to access the slope stability conditions, multivariate statistical techniques have been proved effectiveness to predict stability conditions and hazard of slopes. These methods are capable of creating slope stability prediction systems and defining which variables are more important regarding stability. For applying these techniques, the knowledge of large geotechnical datasets is mandatory. Santos et al. (2018) proposed the Quantitative Hazard Assessment System (HAS-Q) capable of predict the hazard of mine slopes based on multivariate statistical techniques. The HAS-Q was created based on principal component analysis and discriminant analysis.
