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, water flow,
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 openpit
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.