부산대학교 도서관

Abstract: A new Mine Slope Instability Index (MSII) to assess the (in)stability conditions of slopes in open-pit mining is presented. Eighteen parameters that can be easily obtained and rated in the field, and that are important for open-pit slope stability, are employed for the MSII definition. Their corresponding ratings are also proposed, so that the MSII can be computed as a simple weighted sum of ratings for all parameters considered; to minimize subjectivity the weights are computed, in the context of the Rock Engineering Systems paradigm, using an optimized Back-Propagation Artificial Neural Network that has been trained with an extensive database of worldwide open-pit slope stability case histories. Results show that the ANN provides a highly reliable RES interaction matrix, and also that the selected parameters are important for open-pit slope stability. Slope (in)stability hazard levels are defined based on MSII values and the predictions of the newly proposed MSII are validated by comparing our predictions with the actual (i.e. observed) behaviour corresponding to 12 independent case histories that were not used for the ANN training. An excellent agreement between predictions and observations has been found, with only one (out of 12) cases providing an incorrect prediction. [Copyright &y& Elsevier]