Machine learning approach for the prediction of mining-induced stress in underground mines to mitigate ground control disasters and accidents

Abstract

AbstractThe bord and pillar method is commonly employed in Indian underground coal mines, and the extraction rate varies between 30 and 65%. During pillar extraction, pillars are subjected to severe stress conditions. Due to this, the natural state of stress equilibrium is disturbed, resulting in severe strata control problems leading to sudden, unpredictable failure such as a premature collapse of pillars, severe roof or side fall, and sometimes leading to serious/fatal injury or burial of machinery. This paper deals with the prediction of mining-induced stress during pillar extraction using Machine Learning (ML) techniques like Random Forest and Multilayer Perceptron. The various factors used for the formulation of the models for predicting the mining-induced stresses are Depth of the workings (H), Panel width/length (W/L), Pillar width/working height (w/h), Goaf length, and Area of extraction. This paper highlights the importance of operational parameters rather than geological parameters. The Correlation coefficient ($${R}^{2}$$ R 2 ) of mining-induced stresses for the case studies discussed in the paper is 0.85 for Random Forest and 0.76 for Multilayer Perceptron, which shows Random Forest results have a comparative edge over Multilayer perceptron. With this developed prediction models, the stress conditions of pillars can be predicted. Graphic abstract