Abstract
The slopes in open-pit mines are typically excavated to the steepest feasible angle to maximize
profits. However, there is a greater risk of slope failure associated with steeper slopes. An
open-pit slope represents a complex multivariate rock engineering system. Interactions
between the factors affecting slope stability in open pit mines are therefore more complex and
often difficult to define, impeding the use of conventional methods. To address the problem,
the primary role of rock mass structure, in situ stress, waterflow, and construction have been
extended into 18 key parameters. The stability status of slopes and parameter importance are
investigated by means of computational intelligence tools such as Artificial Neural Networks.
An optimized Back Propagation network is trained with an extensive database of 141
worldwide case histories of open-pit mines. The inputs refer to the values of extended
parameters which include 18 parameters relating to open-pit slope stability. The produced
output is an estimated potential for instability. To minimize the subjectivity, the method of
partitioning the connection weights is applied in order to rate the significance of the involved
parameters. The problem of slope stability is therefore modelled as a function approximation.
A new Open-pit Mine Slope Stability Index is thus proposed to assess the potential status
regime from a holistic point of view. These values are validated by computing the predicted
values against the observed status of stability. The reliability of the predictive capability is
computed as the Mean Squared Error, and further validated through a Receiver Operating
Characteristic curve. Together with a Mean Squared Error of 0.0001, and Receiver Operating
Characteristic curve of 98%, the application illustrates that the prediction of slope stability
through Artificial Neural Networks produces fast convergence giving reliable predictions,
and thus being a useful tool at the preliminary feasibility stage of study.