可可盖矿主斜井TBM冒顶机制与识别

doi:10.16265/j.cnki.issn1003-3033.2024.12.1018

摘要/Abstract

摘要：

为探究敞开式隧道掘进机(TBM)在煤矿长大斜井掘进中应对冒顶问题的方法,针对富水、断层、节理、碎裂结构砂岩等不利地质条件这一难题,展开冒顶机制与模式识别研究。首先,运用修正开挖补偿理论和充分考虑中间主应力的围岩最小支护应力分析冒顶机制,并基于可可盖矿主斜井成功案例与TBM现场施工数据,深入分析冒顶特征;然后,依据采集到的现场反馈监测信息,系统研究冒顶前后掘进参数的变化情况,构建随机森林(RF)、反向传播(BP)神经网络、支持向量机库(LIBSVM)机器学习模型以有效识别冒顶。结果表明:碎裂结构层理交错的砂岩水—岩导致砂岩力学性能劣化是冒顶内因,机岩作用能量释放是外因,开挖应力补偿及适时采用钢锚(索)喷+钢拱架(钢板带)不同方案支护是控因;贯入度剧增,滚刀推力、刀盘扭矩、刀盘转速剧减是冒顶掘进参数特征;RF模型对围岩冒顶分类预测精度最好,其识别冒顶风险的准确率比BP、LIBSVM分别提高1.78%、11.84%。

关键词: 主斜井, 隧道掘进机(TBM), 冒顶机制, 中间主应力, 掘进参数, 冒顶风险

Abstract:

To explore the approach for dealing with roof fall by the open TBM in coal mine excavation, the mechanism and pattern recognition of roof fall were investigated considering the unfavorable geological conditions such as abundant water, faults, joints and sandstone with fractured structure. Firstly, the roof fall mechanism was analyzed by utilizing the modified excavation compensation theory and the minimum support stress of surrounding rock which fully considered the intermediate principal stress. Based on the successful case of the main inclined shaft of Kekegai mine and TBM site construction data, the characteristics of roof fall were deeply analyzed. Then, in accordance with the collected on-site feedback monitoring information, the variations of excavation parameters before and after the roof fall were systematically examined, and the machine learning models of random forest (RF), back propagation (BP) neural network, and Library for support vector machines (LIBSVM) were constructed to effectively identify the roof fall. The results demonstrate that the internal cause of roof fall is the deterioration of sandstone mechanical properties resulting from water-rock interlace in the cataclastic structure, the external cause is the energy release by mechanic-rock action, and the controlling cause is the excavation stress compensation and the timely application of steel anchor (cable) shotcrete + steel arch (steel plate belt). The sharp increase of penetration, and thrust of the hob, the torque of the cutter head and sharp decline of the cutter head speed are the characteristics of roof fall driving parameters. The RF model has the highest prediction accuracy for the classification of roof fall of surrounding rock, and its accuracy rate of identifying roof fall risk is 1.78% and 11.84% higher than that of BP and LIBSVM, respectively.

Key words: main inclined shaft, tunnel boring machine (TBM), roof falling mechanism, intermediate principal stress, driving parameters, risk of roof fall

中图分类号:

X936

杨青, 荣传新. 可可盖矿主斜井TBM冒顶机制与识别[J]. 中国安全科学学报, 2024, 34(12): 108-119.

YANG Qing, RONG Chuanxin. Mechanism and recognition of TBM roof fall in main inclined shaft of Kekegai mine[J]. China Safety Science Journal, 2024, 34(12): 108-119.

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模型	类别	精确率	召回率	F₁分数	准确率
BP	NM	98.02	98.41	98.21	96.15
	MM	92.31	88.24	90.23
	RM	85.71	94.74	89.99
RF	NM	98.43	100.00	99.21	97.93
	MM	96.92	92.65	94.74
	RM	94.44	89.47	91.89
LIBSVM	NM	88.34	99.60	93.63	86.09
	MM	83.87	38.24	52.53
	RM	62.5	78.95	69.77