Pacific Journal of Mathematics for Industry
PREDICTIVE INDEX FOR SLOPE INSTABILITIES IN OPEN PIT MINING
Abstract. In this paper we study the stability and deformation of structures, in particular the wall of an open pit mine is studied by using information obtained from a variety of remote sensors and some extra data, with a novelty approach considering the use of mathematical models and data mining techniques. In particular we present two models to help the study the slope stability of pit and the possible occurrence of movements. Primarily we present an static model for slow movements, which will help us identify areas of possible risks areas with time horizons of several months or years, depends on the available information, before the wall start moving, and secondly a dynamic short-term model, which help us to determine risks of collapse zones with several days in advance. We remark that this methodology can be a powerful tool to plain future actions in order to simulate possible scenarios considering the production plans.
A VALIDATION OF THE USE OF DATA SCIENCES FOR THE STUDY OF
SLOPE STABILITY IN OPEN PIT MINES
Abstract. In this work, we present an exploratory study of stability of an open pit mine in the north of Chile with the use of data mining. It is important to note that the study of slope stability is a subject of great interest to mining companies, this is due to the importance in the safety of workers and the protection of infrastructures, whether private or public, in those places susceptible to this kind of phenomena, as well as, for road slopes and close to communities or infrastructures, among others. It is also important to highlight that these phenomena can compromise important economic resources and can even cause human losses. In our case, this study seeks to increase the knowledge of these phenomena and thus, try to predict their occurrence, by means of risk indicators, potentially allowing the mining company supervision to consider predictive measures. It should be considered that there is no online test that ensures timely prediction. In previous studies conducted in other mines, it has been corroborated that the phenomena and factors associated with the movement of slopes and landslides are extremely complex and highly nonlinear, which is why the methods associated with the called data mining, were found to be ideal for discovering new information in the data, which is recorded periodically in the continuous monitoring that mining companies have of their deposits, which allowed to nd important correlations for the search of predictors of these phenomena. Some results, with data coming from dierent sources, are presented at the end of this work. We note that according to the information provided by the mining company, the results were favorable in the indicators of up to six months, giving as risk areas the correct sectors and predicting the April 2017 event.