Slope stability prediction for circular mode failure using gradient boosting machine approach based on an updated database of case histories

Prediction of slope stability is one of the most crucial tasks in mining and geotechnical engineering projects. The accuracy of the prediction is very important for mitigating the risk of slope instability and enhancing mine safety in preliminary design. However, existing methods such as traditional statistical learning models are unable to provide accurate results for slope instability due to the complexity and uncertainties of multiple related factors with small unbalanced data samples thus requiring complex data processing algorithms. To address this limitation, this paper presents a novel prediction method that utilizes the gradient boosting machine (GBM) method to analyze slope stability. The GBM-based model is developed by the freely available R Environment software, trained and tested with the parameters obtained from the detailed investigation of 221 different actual slope cases between 1994 and 2011 with circular mode failure available in the literature. The stability of the circular slope accounts for the unit weight (gamma), cohesion (c), angle of internal friction (phi), slope angle (beta), slope height (H) and pore water pressure coefficient (ru). A fivefold cross-validation procedure is implemented to determine the optimal parameter values during the GBM modeling and an external testing set is employed to validate the prediction performance of models. Area under the curve (AUC), classification accuracy rate and Cohen's Kappa coefficient have been employed for measuring the performance of the proposed model. The analysis of AUC, accuracy together with kappa for the dataset demonstrate that the GBM model has high credibility as it achieves a comparable AUC, classification accuracy rate and Cohen's kappa values of 0.900, 0.8654 and 0.7324, respectively for the prediction of slope stability. Also, variable importance and partial dependence plots are used to interpret the complex relationships between the GBM predictive results and predictor variables.