Cave mine pillar stability analysis using machine learning
DOI:
https://doi.org/10.17159/Abstract
Cave mines represent the largest of the underground mining methods with their scale leading to many challenges, including operational logistics and geomechanics design. In current practice, pillar stability assessment relies almost exclusively on stress analysis. However, stability is also affected by other factors including those related to operational aspects of the mining method whose effects are difficult to account for during the design stages. This paper presents a case study of the application of a machine learning approach used to evaluate the influence of these operational factors on pillar stability at the Chuquicamata underground cave mine in northern Chile.Due to the likely multi-factorial damage process leading to collapses and considering the different pillar conditions, a tree-based machine learning method was used and analyzed to improve the understanding of the relative importance of the various contributing factors. Unlike stress analysis methods, it does not require any a priori knowledge of failure mechanisms nor the calibration of associated controlling parameters. The proposed random forests model predicted pillar collapses with 80% accuracy while having limited samples to model from. The main collapse contributing factors were found to be related to available pillar volume, cave front geometry, and time under abutment stress condition.
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Copyright (c) 2024 ricardo javier quevedo, Yuksel Asli Sari, Stephen D McKinnon
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