基于LSSVR与灰色理论的急倾斜巨厚煤层群开采冒落高度与时滞特征研究

doi:10.13722/j.cnki.jrme.2023.0586

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摘要急倾斜巨厚煤层群水平分段开采易诱发顶板冒落且具有时滞特点，掌握上覆岩层的冒落高度与冒落特征对顶板灾害、裂隙发育和地表塌陷的监测至关重要。为此采用相似模拟实验、机器学习和灰色理论等方法，探究覆岩冒落特征与地表塌陷规律，获取与冒落高度相关的数据指标，以主成分(PCA)分析法对输入数据进行降维处理，通过遗传算法(GA)结合留一交叉验证法优化训练最小二乘支持向量回归(LSSVR)网络建立冒落高度预测模型(PCA-GA-LSSVR)。以开采参数和监测指标对多种组合的预测模型展开训练和测试，评估优选出最佳预测模型并进行工程检验。研究结果表明：在急倾斜巨厚煤层水平分段开采实验中，随开采深度增加岩层的持续冒落会产生并扩大“V”字型塌陷坑；开采深度、单次垮落高度、模型段高等与冒落高度的相关性较高，各指标参数相互之间存在显著的相关性；实验开采中顶板的冒落具有时滞特征；数据经过降维处理可解决模型输入数据维度高信息重叠的问题，从而提升预测模型的准确率，PCA-GA-LSSVR模型对测试数据的预测结果精度最高，其误差、平均绝对误差和平均绝对百分比误差分别为5.146，4.819，0.087都低于其他模型；灰色时滞OBGM模型对45#煤层冒落高度做出了良好的拟合与预测；在工程检验中，建立的冒落高度预测模型其最大误差波动范围在3.36 m之内，小样本数据环境下增加学习样本能减小模型预测的误差。研究成果为急倾斜煤层水平分段开采冒落高度的研究提供一定的借鉴和参考。

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关键词 ：采矿工程, 急倾斜煤层, 水平分段开采, 冒落高度, 最小二乘支持向量回归, 时滞模型

Abstract：In the horizontal sublevel top coal caving of steeply inclined extra-thick coal seam groups，the segmented mining is prone to induce roof collapses with time delayed characteristics. The understanding of the collapse height and characteristics of the overlying strata is crucial for monitoring roof disasters，crack development，and surface collapses. To address this，a combination of similar simulation experiments，machine learning，and grey theory methods were utilized to explore the collapse characteristics of overlying strata and the laws governing surface collapses. Data indicators associated with collapse height were obtained，and the dimensionality of input data was reduced using Principal Component Analysis(PCA). A collapse height prediction model(PCA-GA-LSSVR) based on the least squares support vector regression(LSSVR) network was established through the optimization of training using genetic algorithms(GA) and leave-one-out cross-validation. Various combinations of mining parameters and monitoring indicators were employed for training and testing，facilitating the evaluation and selection of the optimal prediction model. The selected model was further validated through engineering inspections. Results indicate that with increasing mining depth in the experimental mining of steeply inclined extra-thick coal seam groups，continuous collapse of rock strata generates and expands V-shaped subsidence. There are high correlations between mining depth，single caving height，model section height，and collapse height. Additionally，the collapse of the roof in experimental mining exhibits a time delayed characteristic. Dimensionality reduction of data effectively addresses the issue of high dimensionality and information overlap，enhancing the accuracy of prediction models. The PCA-GA-LSSVR model demonstrates superior accuracy in predicting test data，with lower error rates，average absolute errors，and average absolute percentage errors(5.146，4.819，and 0.087 respectively) compared to other models. The established grey time delayed OBGM model exhibits good fitting and predictive capabilities for roof collapse height in the #45 coal seam scenario. During engineering inspection，the maximum error fluctuation range of the established collapse height prediction model is within 3.36 m. In scenarios with limited sample data availability，gradually increasing learning samples reduces model prediction errors. This research provides valuable insights for studying collapse height in steeply inclined extra-thick coal seam groups during horizontal sublevel top coal caving mining.

Key words： mining engineering steeply inclined coal seam horizontal sectional mining collapse height least squares support vector regression time delayed model

引用本文:

崔峰1，2，3，何仕凤1，2，来兴平1，2，刘旭东4，蒋新军4，孙秉成4，贾冲1，2，宗程1，2，李宜霏1，2. 基于LSSVR与灰色理论的急倾斜巨厚煤层群开采冒落高度与时滞特征研究[J]. 岩石力学与工程学报, 2024, 43(4): 822-837.
CUI Feng1，2，3，HE Shifeng1，2，LAI Xingping1，2，LIU Xudong4，JIANG Xinjun4，SUN Bingcheng4，. Study on collapse height and time delayed characteristics in the mining of steeply inclined extra-thick coal seam group based on LSSVR and grey theory. , 2024, 43(4): 822-837.

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http://rockmech.whrsm.ac.cn/CN/10.13722/j.cnki.jrme.2023.0586 或 http://rockmech.whrsm.ac.cn/CN/Y2024/V43/I4/822

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