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 importance of these parameters 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 output is an estimated potential for instability. To minimize subjectivity, the method of partitioning the connection weights is applied in order to rate the significance of the parameters involved. 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 constitutes a useful tool at the preliminary feasibility stage of study.

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