Open stope stability assessment through artificial intelligence

Abstract

Underground mining is a set of methods that allows the extraction of ore in depth, ensuring sustainability and economic viability. One of the problems that arise in underground mine operations is open stope stability. The method for assessing stability of open stopes is the stability graph proposed by Mathews et al. (1981). It is possible to estimate and provide information about this stability and assist in the decision making about its viability. With the data obtained from 35 open stopes from a Zinc mine, the present study aims to use artificial intelligence techniques, specifically artificial neural networks, to process the data and classify the open stopes according to the stability regions of the graph. As a result, the applied methodology presented good assertiveness for the classification of two classes, stable and unstable open stopes, resulting in a global probability success of 82% overall hit probability and 18% apparent error rate. For the classification into three classes, adding the transitional open stopes, the internal validation presented a global probability success of 91% and apparent error rate of 9%. In external validation, the network evaluation measures presented values of global probability success of 42% and apparent error rate of 58%.

Assessment of mine slopes stability conditions using a decision tree approach

“…The MG models use all the variables, and the MM is optimized with a focus on math metrics only. Although MS has a higher error rate, the concentration of dangerous errors is low, 0.22, which can be interpreted as a good selection of variables performed by Santos et al (2020). This result reinforces the importance and impact of variable selection for predictive models.…”

“…The application of machine learning techniques has been increasing in mining, with positive impacts, in recent years. In addition to the studies already mentioned, some studies with machine learning appli-cations can be highlighted are, e.g., Klen & Lana (2014), Silva et al (2018), Baretta et al (2019), Okada et al (2019), Santos et al (2020). The methodology presented allows different users, target audiences in general, to apply the model quickly and accurately, optimizing decisions in mining operations.…”

A Feasibility Study on The Implementation of Neural Network Classifiers for Open Stope Design

“…The literature reveals a number of recent applications of artificial intelligence techniques related to stope design and underground excavation excavations. These include for instance, the design of underground excavation spans using artificial neural network (Wang et al 2002); hard-rock stope span design in entry-type excavations using learning classifiers (García-Gonzalo et al 2016); open stope stability analysis using the random forest algorithm (Qi et al 2018a); prediction of stope stability based on several machine learning algorithms (Qi et al 2018b(Qi et al , 2018c; open stope stability assessment through artificial intelligence (Santos et al 2020); and mine stope performance assessment through classifers (Adoko et al 2019). While in these studies, the focus was mainly on the development of models capable of achieveing a high prediction capability, very limited effort was dedicated to the practical implementation of the ANN for open stope design.…”

Development of a Stope Stability Prediction Model Using Ensemble Learning Techniques - A Case Study

“…Literature revealed that Artificial Intelligence has been less explored in stope stability assessment. Santos et al (2020) conducted similar research to predict the stability of stope using Artificial Neural Network in a Zine mine with data gathered from 35 stopes. However, their method recorded higher misclassified errors of which they attributed to the insufficiency of the data.…”