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
