Assessment of mine slopes stability conditions using a decision tree approach

Abstract

Continuous assessment of slope stability is important to the open pit design and operation. This article aims to present a tool for evaluating the stability conditions of rock slopes in mining, based on a global geotechnical database, using machine learning techniques. Different models are evaluated in this research: the general model, which uses all variables; the mathematical model, which uses only variables selected by the random forest (out-of-bag); and two expert-based models: the Q-Slope model and the Santos model. The validation of the model was done through the test sample, using partition confusion matrices aiming at reproducibility of the results. A study of the types of errors was carried out using Principal Component Analysis (PCA). The study of errors allowed the identification of samples that were inconsistent with the others. Afterwards, the models were redone and compared with the previous ones. The best performers are presented and discussed. The proposed methodology does not replace the classic analysis of slope stability. On the contrary, it contributes to engineers and geologists with a tool for monitoring the stability conditions of slopes in a mining operation. Slope stability analysis must be carried out throughout the mine's lifetime and, therefore, it is believed that the tool proposed here can optimize the selection of slopes most susceptible to instability.