The 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 (\({R}^{2}\)) 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.

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中文翻译：

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用于预测地下矿井采矿引起的应力以减轻地面控制灾害和事故的机器学习方法

印度地下煤矿普遍采用条柱法，开采率在30%至65%之间。在矿柱提取过程中，矿柱承受着严重的应力条件。因此，应力平衡的自然状态受到干扰，导致严重的地层控制问题，导致突然的、不可预测的故障，例如支柱过早倒塌、严重的屋顶或侧面坠落，有时会导致严重/致命的伤害或掩埋。机械。本文利用随机森林和多层感知器等机器学习 (ML) 技术来预测矿柱提取过程中采矿引起的应力。用于制定预测采矿引起的应力的模型的各种因素包括工作深度 (H)、面板宽度/长度 (W/L)、支柱宽度/工作高度 (w/h)、采空区长度、和提取面积。本文强调了操作参数而不是地质参数的重要性。本文讨论的案例研究中采矿引起的应力的相关系数 ( \({R}^{2}\) ) 对于随机森林为 0.85，对于多层感知器为 0.76，这表明随机森林结果具有相对优势多层感知器。利用这个开发的预测模型，可以预测支柱的应力状况。

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