Application and research progress of TBM tunneling in coal mine roadway
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摘要:
TBM工法经济技术优势显著，正成为煤矿巷道快速掘进的一种新方法。但由于煤矿特殊的施工环境和复杂地质条件，TBM在煤矿巷道掘进中面临以下技术挑战：①煤矿特殊施工环境下TBM装备和矿井系统适应性设计难；②煤系软硬复合地层破岩机理不清，高效破岩控制难度大；③软弱地层挤压变形卡机灾害风险大，灾害预测和安全控制难度大；④掘进空间狭小和粉尘水雾干扰严重，TBM掘进过程监测难度大，难以进行掘进参数决策控制和灾害预警。对此，针对TBM装备适应性设计技术，深部复合地层TBM高效破岩理论，挤压变形卡机灾害预测控制方法，掘进过程智能化决策控制技术等开展了系统研究，在TBM安全高效掘进技术方面取得了以下研究进展：①论述了针对煤矿特殊施工环境的TBM装备和施工工艺适应性设计技术；②开展了TBM滚刀贯入和线性切割试验，揭示了复合地层地应力水平、岩石强度及岩性变化、掘进控制模式、滚刀安装半径等对TBM破岩效率和破岩模式的影响机理，提出了深部复合地层TBM掘进性能评价预测方法和岩体可掘性评价方法；③揭示了深部煤系软弱地层TBM掘进挤压大变形卡机灾害孕育发生机理，发展了挤压变形卡机灾害孕育演化及控制过程模拟预测的FDEM（有限元-离散元耦合）方法，提出了挤压变形卡机监测预警方法，形成了TBM掘进挤压大变形卡机“大变径扩挖、掘进参数优化和分步联合支护”综合防控技术体系；④提出了TBM掘进过程岩-机作用信息（刀盘刀具-掘进工作面作用、围岩-护盾作用信息）实时感知技术，初步提出了TBM掘进参数自适应智能决策方法。上述研究进展将推动TBM在煤矿巷道建设中的应用和安全高效掘进技术进步。
Abstract:TBM (Full Face Tunnel Boring Machine) tunneling method has significant economic and technical advantages and is becoming an innovative method for deep roadway speedy construction. However, due to the special complex geological conditions and construction environment of coal mines, the technical challenges faced by TBM tunneling in deep roadways and the key scientific problems are analyzed: ① adaptive design of TBM equipment and the mine system under special tunneling environment in coal mine is difficult to fulfill; ② the rock fragmentation mechanism in soft and hard mixed strata is unclear, and the efficient rock cutting is difficult to realize; ③ The risk of soft rock squeezing deformation and TBM jamming, and the difficulty for accurate prediction and safety control is large; ④ Due to the narrow tunneling space and the serious interference of dust and water fog, it is difficult to monitor the tunneling process and make decision control and disaster warning according to the monitoring information. In this regard, systematic research on adaptive equipment design, efficient rock cutting in mixed-ground, squeezing deformation and TBM jamming disaster prediction and control method, and intelligent assisted tunneling method in deep composite stratum tunneling has been carried out. Research results on the TBM safe and efficient tunneling technology are achieved: ① the present situation of TBM adaptive design technology for special construction environment of coal mine is illustrated. ② full-size disc cutter penetration and linear cutting tests for hard and soft rock under high confining pressure are performed. Accordingly, the influence of complex geo-stress, rock strength and lithology change, tunneling control mode,cutter installation radius on the TBM cutting efficiency and rock fragmentation mechanism are revealed. The evaluation and prediction method of TBM tunneling performance and the corresponding rock mass classification method in deep mixed ground is put forward. ③ The mechanism of large deformation and TBM jamming disaster during TBM tunneling in deep coal measure soft strata is revealed. The FDEM (combined FEM and DEM) numerical simulation method of large deformation and TBM jamming disaster evolution and corresponding control measures for TBM tunneling in soft rock is developed. The monitoring and warning method of squeezing deformation and TBM jamming disaster is put forward. A comprehensive prevention and control technology system of ‘large diameter over excavation, excavation parameter optimization and step by step combined supporting’ is proposed for large squeezing deformation and TBM jamming hazard. ④ The real-time sensing technology of rock-TBM interaction information (cutter head - roadway face interaction, surrounding rock-shield interaction) in TBM tunneling process is proposed, and the adaptive intelligent decision-making method for advance parameters is initially developed. The research progresses will promote the application of TBM tunneling in coal mine roadway construction and safe and efficient tunneling technology.
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0. 引 言
西北生态脆弱区具有显著的“富煤、贫水、弱生态”特征。第四系萨拉乌苏组潜水是该区域惟一具有供水和生态意义水资源。潜水生态水位与区域植被生态、地质环境密切相关[1]。煤炭资源大规模开发，势必扰动生态水位变异，诱发原本脆弱的生态环境进一步恶化。因此，研究生态脆弱区煤层采动下生态水位变异程度对矿区生态地质环境保护、绿色矿山建设具有重要意义。
保水采煤的目标是在防治采场突水的同时，维持具有供水意义和生态价值的含水层稳定或将生态水位变化控制在合理范围内。为此，煤层采动下生态水位变异研究取得了系列成果。现场实测方面，结合地下水传感器监测钻孔潜水水位，分析了潜水位下降与煤层开采强度的关系[2]、潜水位波动与地表沉陷的耦合关系[3]、潜水位下降对干旱矿区植被蒸腾[4]及河岸区地下水蒸散发量的影响[5]、地下水位与干旱区生态系统关系[6]、保水采煤环境工程地质模式及保水采煤类型区域研究[7]。钻孔水位监测较难实现大区域全方位、小尺度、精细化监测，为此，结合数值模拟，分析了采矿活动和降雨事件对潜水位的影响规律[8–10]、预测了矿井涌水量及评价了矿区地下水资源[11–13]、研究了高强度煤层采动下最佳生态水位将至警戒水位的生态效应[14]，弥补了钻孔水位监测的不足。此外，基于偏差信息和非线性自回归神经网络混合模型[15]、创新趋势分析和Mann-Kendall方法[16]、频谱法和校正方法、时间序列分析法[17]等数学理论方法，在选择多个影响因素的基础上，预测采矿活动对地下水位的影响，但缺乏水位变化的物理意义。采矿活动影响下地下水演变机理方面，Abdullah Karaman等假设将采煤工作面看作移动抽水井，模拟了地面沉降对地下水位的影响[18]；随后采用类似曲线分析计算法建立了采矿速率、水力扩散系数与潜水水位的耦合关系[19]，探究了采矿活动影响下地下水位的演变规律。杨倩等[20]阐述了采动裂隙导水和变形压力作用下的承压水位变化机制，建立了地下水稳定运动、非稳定运动2种井流方程的覆岩承压含水层水位采动变化数学模型。李涛等[21]利用Theis公式与相似模拟试验相结合的方法，建立了煤层采动对地下水位恢复的井流预测模型。
当前研究集中于煤层采动潜水渗漏条件下生态水位如何下降，而往往忽略潜水不渗漏/采煤沉陷扰动下生态水位恢复演变规律。此外，采动生态水位演变往往依靠现场实测，水位恢复程度预测解析解不明确。基于此，笔者首先基于“关键层位置+薄板理论+土拱效应+下行裂隙”建立煤层采动下覆岩–土隔水层厚度计算方法，判别潜水渗漏状态；其次，结合2个工作面的潜水生态水位实测数据，研究煤层采动下潜水生态水位变化规律；然后，基于井流模型建立采煤沉陷扰动下生态水位恢复程度解析解；最后，对比分析生态水位恢复实测值与解析值，并探讨了生态水位未完全恢复的原因。
1. 煤层采动下潜水渗漏状态判别
1.1 覆岩残余隔水层厚度的确定方法
随着我国东部煤炭资源逐渐枯竭，煤炭开采重心已转移至西北生态脆弱区。西部地区主采侏罗系煤层，地表被第四系沙层所覆盖，其下的第四系萨拉乌苏组潜水含水层具有重要的生态意义。第四系潜水隔水层主要是新近系保德组红土与中更新统离石组黄土，西部地区地层综合柱状图如图1所示。
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图 1 西部矿区地层综合柱状图Figure 1. Comprehensive column chart of strata in the western mining area煤层采动后，形成覆岩导水裂隙带。目前煤层开采导水裂隙带发育高度(简称“导高”)预计的方法，主要针对我国东部石炭–二叠系煤层开采实测值总结。东部矿区主要以深井开采为主，而西部矿区开采主要为浅埋厚煤层，具有大采高、大采深的特点，上覆潜水隔水土层主要以黄土、红土为主，导水裂隙带易贯穿基岩进入(部分)土层，与东部矿区煤层埋藏以及开采条件大有不同，先前总结的经验公式难以实现西部矿区导高的准确预测[22–24]。
基于上述分析，针对西部矿区特征，结合已有研究成果，基于“关键层位置+薄板理论+土拱效应+下行裂隙”建立了覆岩–土结构下采动导高计算模型(图2)。首先，当覆岩当关键层位置距开采煤层大于7～10倍煤层采厚距离时，导高等于该关键层距开采煤层的高度[25]。反之，基于薄板理论计算主关键层上部基岩破断情况，即：通过对比薄板的极限挠度值与岩层下部自由空间高度的大小，判断各基岩岩层是否全部断裂，确定发育在基岩中的导高。若上部基岩岩层全部断裂，则需要判断导水裂隙带是否继续向上发育至土层。为此，基于普氏理论和岩体极限平衡理论确定土体破坏临界高度[26]。如果土层的厚度足够大，当土层被破坏到临界高度时，就会形成一个自然平衡拱，此时土层就不会继续垮落，导水裂隙带发育高度即为临界高度与基岩厚度之和。如果土层没有形成稳定拱，考虑采煤沉陷下行裂隙深度是否发育至基岩顶界面[27]，若两者贯通，导水裂隙带贯穿地表；若没有发育至基岩则导水裂隙带发育到土–岩交界面。综上，确定覆岩–土结构下采动导水裂隙带发育高度，为下述潜水渗漏状态判别提供理论依据。
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图 2 覆岩–土结构下采动导水裂隙带高度计算流程Figure 2. Flow of calculating the height of water fracture zone under overburden bedrock and soil structure1.2 考虑残余隔水层厚度的潜水渗漏状态判别
在确定覆岩–土结构下采动导高的基础上，确定覆岩残余隔水层厚度及其类型。为保证煤矿开采安全，通常根据松散层、黏性土层厚度以及采厚等因素留设防水或防砂安全煤岩柱，但是对于考虑残余隔水层厚度及其岩性组合的潜水渗漏状态无法判别。
为此，对于覆岩残余隔水层厚度与渗漏状态的确定，笔者在分析潜水渗漏模式阈值和残余隔水层厚度组合线性变化规律的基础上，确定潜水渗漏与否的阈值：红土、黄土和基岩厚度分别为22、36和120 m[26]。李涛等[28]采用水–电相似模拟得出：离石黄土42.6 m或保德红土21.0 m为潜水不发生渗漏的最小厚度。综上，对比分析残余隔水层厚度与渗漏阈值(表1)，确定潜水的渗漏状态。
表 1 潜水渗漏模式判断标准Table 1. Criteria for judging aquifer leakage modes渗漏情况 判断方法1 判断方法2 潜水不发生渗漏 $ \dfrac{x}{{22}} + \dfrac{y}{{36}} + \dfrac{{\textit{z}}}{{120}} - 1 \geqslant 0 $ $ \dfrac{x}{{42.6}} + \dfrac{y}{{21}} - 1 \geqslant 0 $ 潜水发生渗漏 $ \dfrac{x}{{22}} + \dfrac{y}{{36}} + \dfrac{{\textit{z}}}{{120}} - 1 < 0 $ $ \dfrac{x}{{42.6}} + \dfrac{y}{{21}} - 1 < 0 $ 注：x为残余红土厚度，m；y为残余黄土厚度，m；z为残余基岩厚度，m。 2. 采动沉陷扰动下生态水位实测
2.1 煤层采动潜水渗漏状态
在西北生态脆弱区金鸡滩煤矿108工作面和小保当煤矿01工作面分别开展采煤扰动下生态水位实测。为判断潜水渗漏状态，在108工作面布置了KY1、KY2两个钻孔实测煤层采动导高分别为170.55、178.45 m[14](图3)。在钻孔KY1中，残余基岩和土层的厚度分别为9.26、39.7 m。在钻孔KY2中，残余基岩和土层的厚度分别为19.16、26.8 m。对于01工作面，布置了钻孔S5测试导高为157.39 m[3](图4)，残余隔水基岩和土层的厚度分别为74.07、65 m。
此外，结合覆岩残余隔水层厚度的确定方法和工作面综合钻孔柱状图，确定01、108工作面覆岩主关键层与开采煤层的距离均大于7～10倍煤层采厚，基于此，导高等于该关键层距开采煤层的高度分别为：179.13、191.76 m。结合工作面综合柱状图，108工作面残余基岩和土层的厚度分别为10.58、33.25 m；01工作面残余基岩和土层的厚度分别为52.33、65 m。
结合上述潜水渗漏状态参与隔水层厚度阈值，确定01、108工作面煤层采动过程中潜水均未发生渗漏。
2.2 煤层采动下生态水位实测
为了监测工作面整个采煤期间潜水位变化，在108工作面设置了水位监测钻孔KY8和KY10(图3)。监测钻孔KY8位于工作面倾斜方向的中心，距离工作面终采线170 m；监测钻孔KY10位于108工作面运输巷上方，距离工作面终采线117 m (图3)。01工作面布置S5和S13三个水文监测钻孔(图4)。同时，监测各个钻孔孔口的地面沉降值。
KY8钻孔于2017–12–06开始监测，此时采煤工作面煤壁与监测钻孔KY8的水平距离为3.4 m。根据图5可将KY8水位及地面沉降变化分为4个阶段，分别为1—水位快速下降阶段；2—受地面迅速沉降影响水位短暂平稳阶段；3—地面沉降幅度减弱、水位缓慢上升阶段；4—地面沉降幅度进一步减小直至沉降稳定、水位逐渐稳定阶段。工作面过孔后24 d左右水位下降至最低点，采煤进尺远离钻孔并运动到终采线后钻孔处沉降幅度逐渐减小并趋于稳定，水位逐渐恢复。
01工作面S5钻孔于2019–04–10开始观测，潜水位随回采均表现出“先下降后回升”的规律，如图6所示。采煤工作面过孔后25 d左右各水文孔达到最低水位，与地表下沉具有同时性，即地表开始下沉时，潜水位开始下降，下降幅度基本一致。采后138 d左右采煤进尺远离钻孔并运动到终采线后钻孔处沉降幅度逐渐减小并趋于稳定，水位逐渐恢复[3]。
2.3 生态水位演变规律
根据01和108工作面采动潜水变化实测可知，对于某一观测点来说，地面沉降与水位下降同步，地面沉降的幅度将影响潜水位的下降速度：地表沉降活跃期，地表沉降速度急剧加快，甚至大于水位下降幅度，潜水位迅速下降；当地表沉降进入活跃阶段后期，地面沉降幅度逐渐减弱，潜水位逐渐稳定；地面沉降衰退阶段，地表沉降趋于稳定，潜水位易在大气降水和潜水侧向补给作用下逐渐回升，进入回升阶段。
因此，在煤层采动潜水不渗漏/采煤沉陷扰动条件下，生态水位呈现“迅速下降—缓慢回升—趋于稳定”的演化规律，但采后潜水水位通常不能完全恢复至采前水位。
3. 采煤沉陷扰动下生态水位恢复程度井流模型解析
为模拟测孔的潜水位在采煤后恢复所需要的时间，建立满足Theis公式的单井数学模型。将模拟恢复时间与上述实际测量恢复时间进行对比，研究采煤对水位波动的影响规律。
3.1 井流模型的建立
随着采煤进尺经过监测点并运动到停采线水位下降，将此过程的瞬时水位看作一口虚拟抽水井(图7)，以定流量Q在tp时间内抽水造成潜水位降落漏斗；而潜水位的恢复发生在采煤结束地面沉降稳定停止虚拟抽水后，将这一过程看作虚拟注水。因此潜水位降深s′与时间t的关系如图8所示，图中t′为水位恢复的时间，sr′为水位恢复期的修正剩余降深[29-30]。
3.2 井流模型的解析
数学模型的建立采用定流量的Theis公式
$$ s' = \frac{Q}{{4\pi T}}W(u) $$ (1) $$ W(u) = \int_u^\infty {\frac{{{{\mathrm{e}}^{ - y}}}}{y}} {\mathrm{d}}y $$ (2) $$ u = \frac{{{r^2}\mu }}{{4Tt}} $$ (3) $$ W(u) = \ln \frac{{2.25Tt}}{{{r^2}\mu }} $$ (4) 式(3)、(4)为Theis公式的简化形式。式中，s′为虚拟抽水井的降深，m；r为钻孔到虚拟抽水井的距离，m；u为含水层的给水度； T为含水层导水系数，m2/d，T=KM，K为含水层的渗透系数，m/d，M为初始含水层厚度，m；t为虚拟抽水开始到计算时刻的时间，d ；Q为虚拟抽水井的定流量，m3/d；W(u)为井函数；y为积分变量。
首先计算虚拟抽水流量Q，选取模型中非对称的两任意观测点，已知其虚拟抽水结束后的水位s1's2'，假定虚拟抽水井位置，预设r1 r2，结合井式(1)得到式(5)，即可以求出Q值，再把求出的Q值代入Jacob式(6)，得到近似的虚拟抽水进行的时间tp。
$$ K = \frac{{0.318\;3Q(\ln \;{r_2} - \ln \;{r_1})}}{{(2M - {s_1}' - {s_2}')({s_1}' - {s_2}')}} $$ (5) $$ s' = \frac{{0.183Q}}{T}\lg \frac{{2.25T{t_{\mathrm{p}}}}}{{{r^2}\mu }} $$ (6) 根据计算出的Q值和tp值代入恢复期的Thies式(7)，其等价于同流量的虚拟抽水和虚拟注水的叠加，并代入任意降深可以求出对应的恢复所用时间t'。
$$ {s_{\mathrm{r}}}' = \frac{Q}{{4\pi T}}\left(\ln \frac{{t' + {t_{\mathrm{p}}}}}{{t'}} - \frac{{{r^2}\mu }}{{4Tt'}}\right) $$ (7) 根据井流模型解析生态水位波动过程的流程如图9所示。
4. 采煤沉陷扰动下生态水位恢复程度预测
4.1 生态水位恢复实测值与预测值对比分析
根据两工作面已有的工程地质、水文地质参数，结合2.2节中的水位监测数据，使用3.2节中所述的方法，能够对108工作面和01工作面的生态水位恢复程度进行预测解析。
01工作面中，$ K = 3.5\;{\mathrm{m/d}}，M = 10\;{\mathrm{m}}，T = KM = 35\;{{\mathrm{m}}}^{2}/{\mathrm{d}}，\mu =0.27 $。选取2.2节中测量出的S5和S13钻孔的水位降深以及其距离虚拟井的水平距离$ s'_1=2.595\;{\mathrm{m}}，s'_2=1.679\;{\mathrm{m}}，{r}_{1}=120\;{\mathrm{m}}，{r}_{2}= 128.5\;{\mathrm{m}} $为观测数据代入式(5)计算出虚拟抽水井的流量Q=2 314.479 m3/d。将流量Q代入抽水阶段的Jacob式(6)得出虚拟抽水时间$ {t}_{p}=26.94\;{\mathrm{d}} $，S5观测井实际抽水时间为27 d (图10中的I阶段)。S5水位恢复到第1个峰值时的水位降深为1.635 m，将虚拟抽水井流量Q和虚拟抽水时间tp代入式(7)可以得出虚拟注水时间t'=27.37 d，实际恢复时间为26 d (图10中的II–1阶段)；恢复到最高水位时的水位降深为1.081 m，虚拟注水时间t'=64.26 d，实际恢复时间为63 d (图10中的II–2阶段)。
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图 10 S5钻孔水位波动预测时间与实际时间对比Figure 10. Comparison between predicted time and actual time of water level fluctuation in S5 borehole108工作面KY8钻孔的生态水位恢复过程解析[30]，得出KY8观测井虚拟抽水时间$ {t}_{{\mathrm{p}}}=243.988\;{\mathrm{d}} $，实际抽水时间为244 d(图11中的I阶段)。KY8观测井虚拟水位恢复到60%时，得$ t'_{60\%}=23.715\;{\mathrm{d}} $；KY8观测井实际恢复时间为20 d左右(图11中的II–1阶段)；恢复到110%时$ s'_{1\tau }=2.267\;{\mathrm{m}} $， $ t'_{110\%}= 42.127\;{\mathrm{d}} $；实际为40 d左右(图11中的II–2阶段)；恢复到200%时，$ t'_{200\%}=78.178\;{\mathrm{d}} $；实际为80 d左右(图11中的II–3阶段)。
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图 11 KY8钻孔水位波动预测时间与实际时间对比Figure 11. Comparison between predicted time and actual time of water level fluctuation in KY8 borehole以上模拟过程的计算结果与实测结果之间存在一定误差，主要来源于计算过程中对井公式的简化带来的误差，以及开采过程中地面沉降带来的上覆隔水层的变化会对结果造成影响。但是误差较小，因此可以通过建立井流模型与实际测量数据相结合来估计采煤后的水位恢复时间。
4.2 生态水位未完全恢复原因
综合现场监测和解析模型生态水位变化数据可知：潜水不渗漏情况下采动生态水位下降主要由采煤沉降导致。随着地面沉降速度下降并趋于0，生态水位会有一定程度恢复，但通常在相当长时间内不能完全恢复至采前生态水位。笔者认为主要由地表地形地貌、大气降雨补给、潜水含水层补径排、矿区井下疏放水等综合因素诱发采后生态水位未能完全恢复。
其中，地形地貌控制着潜水水位的分布特征，潜水面形态与地表形态基本一致，采煤扰动下地表地形、微地貌均会发生变化，尤其在采煤沉陷盆地范围内，生态水位也相应发生变化。若煤层采动区域内潜水含水层分布局限，侧向补给缺乏[3]，或大气降水补给条件较差，无法弥补由于沉陷引起的生态水位下降程度就会导致采煤沉陷扰动下生态水位无法完全恢复。为了防治矿井水害，井下通常采取钻孔疏放水的措施，易诱发区域地下水系统中各含水层出现不同程度的补给排泄，进而会导致生态水位未能恢复至采前状态。
5. 结 论
1)针对西部矿区特征，基于关键层位置+薄板理论+土拱效应+下行裂隙建立了采动覆岩–土结构下导水裂隙带高度计算模型，结合覆岩残余基岩和土层阈值，构建了煤层采动下潜水渗漏状态判别方法。
2)在煤层采动潜水不渗漏/采煤沉陷扰动条件下，生态水位呈现迅速下降→缓慢回升→趋于稳定的演化规律，但采后潜水水位往往不能完全恢复至采前水位。
3)建立了采煤沉陷扰动下生态水位恢复程度井流解析模型，对比实测结果表明：生态水位不同恢复程度的恢复时间解析值与实测值误差均小于10%。
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图 4 TBM滚刀破岩机理
FN−滚刀法向力；S−滚刀间距；β−岩石破碎角的一半；W−岩渣宽度。
Figure 4. Rock fragmentation mechanism under TBM cutting
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图 6 深部软弱地层TBM掘进挤压变形卡机灾害示意
Figure 6. Squeezing deformation and TBM jamming disaster when tunneling in deep and soft ground
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图 7 采用免焊接螺栓的分体式刀盘适应性设计
Figure 7. Adaptive design of split cutter-head using bolts without welding
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图 10 围压对破岩效率的影响规律
Figure 10. Influence law of confining pressure on rock cutting efficiency
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图 11 岩体可掘性指数随滚刀安装半径的变化规律
Figure 11. Variation law of rock mass boreability index with cutter installation radius
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图 12 TBM开挖围岩破裂碎胀挤压变形机理
Figure 12. Surrounding rock squeezing deformation mechanism due to fragmentation and bulking after TBM excavation
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图 13 围岩与护盾相互作用致灾过程
Figure 13. Surrounding rock – shield interaction and the induced disaster process
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图 14 FDEM将块体划分成三角形单元及中间插入起黏结作用的节理单元
Figure 14. Block is divided into triangular finite elements and crack elements in FDEM model
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图 18 护盾区域围岩收敛变形监测技术
Figure 18. Monitoring technology for surrounding rock convergence in shield area
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图 19 护盾挤压力阵列式感知方法
Figure 19. Array sensing method for shield surrounding rock pressure acting on shield
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图 20 护盾挤压力网格覆盖法有限元反演方法
Figure 20. Mesh covering-based finite element inversion method for pressures on shield
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图 22 深部巷道分步联合控制作用原理
Figure 22. Principle of step-by-step combined supporting method for deep roadway
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图 23 挤压变形卡机灾害综合防控技术体系
Figure 23. Comprehensive prevention and control technology system for squeezing deformable and TBM jamming disaster
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图 26 TBM掘进岩-机作用实时感知系统
Figure 26. Real-time monitoring system for surrongding rock and TBM interaction
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图 29 TBM掘进参数QPSO-ANN实时预测模型
Figure 29. QPSO-ANN model for TBM advance parameter real-time prediction
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图 30 掘进控制参数智能决策算法流程
Figure 30. Flow chart of intelligent decision algorithm for advance control parameters
表 1 岩体TBM可掘性分级
Table 1 Rock mass boreability grading in TBM tunneling
岩体可掘性分级 FPI(kN/Cutter/
mm/rev)岩体可掘性程度 建议推力与最大推力百分比/% 建议转速与最大转速百分比/% 1 <20 极好 45 75 2 20~30 很好 60 90 3 30~40 好 75 100 4 40~50 中等 90 95 5 50~60 差 95 95 6 60~70 很差 100 95 7 ≥70 极差 100 90 
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[1] 何满潮,谢和平,彭苏萍,等. 深部开采岩体力学研究[J]. 岩石力学与工程学报,2005,24(16):2803−2812. doi: 10.3321/j.issn:1000-6915.2005.16.001 HE Manchao,XIE Heping,PENG Suping,et al. Study on rock mechanics in deep mining engineering[J]. Chinese Journal of Rock Mechanics and Engineering,2005,24(16):2803−2812. doi: 10.3321/j.issn:1000-6915.2005.16.001
[2] 岩石隧道掘进机(TBM)施工及工程实例[M]. 北京: 中国铁道出版社, 2004: 337. Full Face rock tunnel boring machine (TBM) construction and engineering example [M]. Beijing: China Railway publishing House, 2004: 337.
[3] 陈 馈, 孙振川, 李 涛. TBM设计与施工[M]. 北京: 人民交通出版社股份有限公司, 2018. CHEN Kui, SUN Zhenchuan, LI Tao. TBM design and Construction [M]. Beijing: China Communications Press Co. Ltd. , 2018.
[4] LIU Quansheng,HUANG Xing,GONG Qiuming,et al. Application and development of hard rock TBM and its prospect in China[J]. Tunnelling and Underground Space Technology,2016,57:33−46. doi: 10.1016/j.tust.2016.01.034
[5] ZHENG Yanlong,ZHANG Qianbing,ZHAO Jian. Challenges and opportunities of using tunnel boring machines in mining[J]. Tunnelling and Underground Space Technology,2016,57(4):287−299.
[6] JING Liujie,LI Jianbin,ZHANG Na,et al. A TBM advance rate prediction method considering the effects of operating factors[J]. Tunnelling and Underground Space Technology,2021,107:103620. doi: 10.1016/j.tust.2020.103620
[7] LIU Bin,WANG Ruirui,GUAN Zengda,et al. Improved support vector regression models for predicting rock mass parameters using tunnel boring machine driving data[J]. Tunnelling and Underground Space Technology,2019,91:102958. doi: 10.1016/j.tust.2019.04.014
[8] 刘泉声,黄 兴,时 凯,等. 煤矿超千米深部全断面岩石巷道掘进机的提出及关键岩石力学问题[J]. 煤炭学报,2012,37(12):2006−2013. doi: 10.13225/j.cnki.jccs.2012.12.029 LIU Quansheng,HUANG Xing,SHI Kai,et al. Utilization of full face roadway boring machine in coal mines deeper than 1 000 km and the key rock mechanics problems[J]. Journal of China Coal Society,2012,37(12):2006−2013. doi: 10.13225/j.cnki.jccs.2012.12.029
[9] HUANG Xing,LIU Quansheng,SHI Kai,et al. Application and prospect of hard rock tbm for deep roadway construction in coal mines[J]. Tunnelling and Underground Space Technology,2018,73:105−126. doi: 10.1016/j.tust.2017.12.010
[10] HANDEWITH H. J. TBM tunnels in the western hemisphere-an overview [J]. Tunneling Technology Newsl. 1983, 41: 1-8.
[11] STACK B. Handbook of mining and tunnelling machinery [M]. 1982, J. Wiley.
[12] ROBBINS R. J. Future of mechanical excavation in underground mining[J]. Mining Engineering,1984,36:617−627.
[13] CIGLA M, YAGIZ S, OZDEMIR L. Application of tunnel boring machines in underground mine development [A]. 17th International Mining Congress and Exhibition of Turkey [C]// Ankara, Turkey, 2001: 155−164.
[14] HOME L, ASKILSRUD O.G. Tunnel boring machines in mining (Third edition) [M]. Darling P. (Ed.), SME Mining Engineering Handbook, USA: SME, 2011, pp. 1255−1270.
[15] BROX D. Technical considerations for the application of TBMs for mining projects. Trans. Soc. Mining Metall. Explor. 2013, 334, 498−505.
[16] 唐 彬,程 桦,姚直书,等. TBM施工煤矿深埋硬岩巷道围岩稳定性分析及工程应用[J]. 采矿与安全工程学报,2016,33(2):260−264. TANG Bin,CHENG Hua,YAO Zhishu,et al. Stability analysis and engineering application of TBM deep coal mine roadway and surrounding rock[J]. Journal of Mining & Safety Engineering,2016,33(2):260−264.
[17] TANG Bin,CHENG Hua,TANG Yongzhi,et al. Experiences of gripper TBM application in shaft coal mine: A case study in Zhangji coal mine, China[J]. Tunnelling and Underground Space Technology,2018,81:660−668. doi: 10.1016/j.tust.2018.08.055
[18] 杨生华,芮 丰,蒋卫良,等. 煤矿全断面岩巷掘进机开发应用与发展[J]. 煤炭科学技术,2019,47(6):1−10. doi: 10.13199/j.cnki.cst.2019.06.001 YANG Shenghua,RUI Feng,JIANG Weiliang,et al. Development and application of full-section rock tunneling boring machine in coal mine[J]. Coal Science and Technology,2019,47(6):1−10. doi: 10.13199/j.cnki.cst.2019.06.001
[19] 程 桦,唐 彬,唐永志,等. 深井巷道全断面硬岩掘进机及其快速施工关键技术[J]. 煤炭学报,2020,45(9):3314−3324. CHENG Hua,TANG Bin,TANG Yongzhi,et al. Full face tunnel boring machine for deep-buried roadways and its key rapid excavation technologies[J]. Journal of China Coal Society,2020,45(9):3314−3324.
[20] 张洪伟,胡兆锋,程敬义,等. 深部高温矿井大断面岩巷TBM智能掘进技术:以“新矿1号”TBM为例[J]. 煤炭学报,2021,46(7):2174−2185. doi: 10.13225/j.cnki.jccs.jj21.0579 ZHANG Hongwei,HU Zhaofeng,CHENG Jingyi,et al. TBM techniques for intelligent excavating large-section rock roadway in the deep high-temperature coal mines: Application of TBM in Xinkuang No. 1[J]. Journal of China Coal Society,2021,46(7):2174−2185. doi: 10.13225/j.cnki.jccs.jj21.0579
[21] 代恩虎,王 勇,鲁义强,等. 云贵地区煤矿TBM施工技术应用研究[J]. 煤炭科学技术,2022,50(10):10−18. DAI Enhu,WANG Yong,LU Yiqiang,et al. Study on application of TBM construction technology in coal mine in Yunnan-Guizhou area[J]. Coal Science and Technology,2022,50(10):10−18.
[22] 范京道,封 华,宋朝阳,等. 可可盖煤矿全矿井机械破岩智能化建井关键技术与装备[J]. 煤炭学报,2022,47(1):499−514. FAN Jingdao,FENG Hua,SONG Zhaoyang,et al. Key technology and equipment of intelligent mine construction of whole mine mechanical rock breaking in Kekegai Coal Mine[J]. Journal of China Coal Society,2022,47(1):499−514.
[23] 昝志华. 提升煤矿盾构机(TBM)在巷道掘进过程中的支护效率[J]. 煤矿现代化,2021,30(5):65−68. doi: 10.3969/j.issn.1009-0797.2021.05.023 ZAN Zhihua. Improving the support efficiency of coal mine shield machine (TBM) in the process of tunnel excavation[J]. Coal Mine Modernization,2021,30(5):65−68. doi: 10.3969/j.issn.1009-0797.2021.05.023
[24] TOTH A,GONG Q M,ZHAO J. Case studies of TBM tunnelling performance in rock-soil interface mixed ground[J]. Tunnelling and Underground Space Technology,2013,38:140−150. doi: 10.1016/j.tust.2013.06.001
[25] GONG Qiuming,YIN Lijun,MA Hongsu,et al. TBM tunnelling under adverse geological conditions: An overview[J]. Tunnelling and Underground Space Technology,2016,57:4−17. doi: 10.1016/j.tust.2016.04.002
[26] 罗超文,李海波,刘亚群. 煤矿深部岩体地应力特征及开挖扰动后围岩塑性区变化规律[J]. 岩石力学与工程学报,2011,30(8):1613−1618. LUO Chaowen,LI Haibo,LIU Yaqun. Characteristics of in-situ stress and variation law of plastic zone of surrounding rocks around deep tunnels in a coal mine[J]. Chinese Journal of Rock Mechanics and Engineering,2011,30(8):1613−1618.
[27] BARTON A A N. TBM tunnelling in jointed and faulted rock [M]. Rotterdam: Balkema, 2000, 61−64.
[28] 周建军,杨振兴. 深埋长隧道TBM施工关键问题探讨[J]. 岩土力学,2014,35(S2):299−305. doi: 10.16285/j.rsm.2014.s2.049 ZHOU Jianjun,YANG Zhenxing. Discussion on key issues of TBM construction for long and deep tunnels[J]. Rock and Soil Mechanics,2014,35(S2):299−305. doi: 10.16285/j.rsm.2014.s2.049
[29] 尚彦军,杨志法,曾庆利,等. TBM施工遇险工程地质问题分析和失误的反思[J]. 岩石力学与工程学报,2007,26(12):2405−2411. doi: 10.3321/j.issn:1000-6915.2007.12.004 SHANG Yanjun,YANG Zhifa,ZENG Qingli,et al. Retrospective analysis of TBM accidents from its poor flexibility to complicated geological conditions[J]. Chinese Journal of Rock Mechanics and Engineering,2007,26(12):2405−2411. doi: 10.3321/j.issn:1000-6915.2007.12.004
[30] 尚彦军,史永跃,曾庆利,等. 昆明上公山隧道复杂地质条件下TBM卡机及护盾变形问题分析和对策[J]. 岩石力学与工程学报,2005,24(21):3858−3863. doi: 10.3321/j.issn:1000-6915.2005.21.009 SHANG Yanjun,SHI Yongyue,ZENG Qingli,et al. TBM Jamming and deformation in complicated geological conditions and engineering measures[J]. Chinese Journal of Rock Mechanics and Engineering,2005,24(21):3858−3863. doi: 10.3321/j.issn:1000-6915.2005.21.009
[31] EVERT HOEK,PAUL MARINOS. Predicting tunnel squeezing problems in weak heterogeneous rock masses[J]. Tunnels and Tunnelling International,2000,32(11):45−51.
[32] European Construction Technology Platform, Strategic research agenda for the European underground construction sector. www. ectp. org. 2005.
[33] LI Jianbin,JING Liujie,ZHENG Xiaofeng,et al. Application and outlook of information and intelligence technology for safe and efficient TBM construction[J]. Tunnelling and Underground Space Technology,2019,93:103097. doi: 10.1016/j.tust.2019.103097
[34] 龚秋明,何冠文,赵晓豹,等. 掘进机刀盘滚刀间距对北山花岗岩破岩效率的影响实验研究[J]. 岩土工程学报,2015,37(1):54−60. doi: 10.11779/CJGE201501005 GONG Qiuming,HE Guanwen,ZHAO Xiaobao,et al. Influence of different cutter spacings on rock fragmentation efficiency of Beishan granite by TBM[J]. Chinese Journal of Geotechnical Engineering,2015,37(1):54−60. doi: 10.11779/CJGE201501005
[35] 龚秋明,张 浩,李 真,等. 机械破岩试验平台研制[J]. 现代隧道技术,2016,53(2):17−25. doi: 10.13807/j.cnki.mtt.2016.02.003 GONG Qiuming,ZHANG Hao,LI Zhen,et al. Development of a Testing Platform for Mechanical Rock Breaking[J]. Modern Tunnelling Technology,2016,53(2):17−25. doi: 10.13807/j.cnki.mtt.2016.02.003
[36] PAN Yucong,LIU Quansheng,PENG Xingxin,et al. Full-scale linear cutting tests to propose some empirical formulas for TBM disc cutter performance prediction[J]. Rock Mechanics and Rock Engineering,2019,52:4763−4783. doi: 10.1007/s00603-019-01865-x
[37] PAN Yucong,LIU Quansheng,LIU Qi,et al. Full-scale linear cutting tests to check and modify a widely-used semi-theoretical model for disc cutter cutting force prediction[J]. Acta Geotechnica,2020,15:1481−1500. doi: 10.1007/s11440-019-00852-4
[38] MACIAS F. J. Hard rock tunnel boring: Performance predictions and cutter life assessments [D]. Trondheim: Norwegian University of Science and Technology, 2016.
[39] ROSTAMI J. , OZDEMIR L. A new model for performance prediction of hard rock TBMs [C]//Proceedings of Rapid Excavation and Tunneling Conference. USA, 1993, 794−809.
[40] LIU Quansheng,LIU Jianping,PAN Yucong,K,et al. A case study of TBM performance prediction using a Chinese rock mass classification system-Hydropower Classification (HC) method[J]. Tunnelling and Underground Space Technology,2017,65:140−154. doi: 10.1016/j.tust.2017.03.002
[41] RAMONI G,ANAGNOSTOU G. The Interaction Between Shield, Ground and Tunnel Support in TBM Tunnelling Through Squeezing Ground[J]. Rock Mechanics and Rock Engineering,2011,44:37−61. doi: 10.1007/s00603-010-0103-8
[42] 刘泉声,黄 兴,时 凯,等. 深部挤压性地层TBM掘进卡机孕育致灾机理[J]. 煤炭学报,2014,39(S1):75−82. doi: 10.13225/j.cnki.jccs.2012.1382 LIU Quansheng,HUANG Xing,SHI Kai,et al. The mechanism of TBM shield jamming disaster tunnelling through deep squeezing ground[J]. Journal of China Coal Society,2014,39(S1):75−82. doi: 10.13225/j.cnki.jccs.2012.1382
[43] ANTE MUNJIZA. The combined finite-discrete element method [M]. London: John Wiley & Sons, Ltd. , 2004.
[44] LISJAK A,GRASSELLI G. A review of discrete modeling techniques for fracturing processes in discontinuous rock masses[J]. Journal of Rock Mechanics and Geotechnical Engineering,2014,6:301−314. doi: 10.1016/j.jrmge.2013.12.007
[45] 邓鹏海. 深部软弱地层TBM掘进挤压变形卡机及防控过程FDEM数值模拟研究[D]. 武汉: 武汉大学, 2019. DENG Penghai. FDEM numerical simulation study on the TBM jamming and prevention-control process due to squeezing deformation in deep soft ground [D]. Wuhan: Wuhan University, 2019.
[46] LIU He,LIU Quansheng,MA Hao,et al. A novel GPGPU-parallelized contact detection algorithm for combined finite-discrete element method[J]. International Journal of Rock Mechanics and Mining Sciences,2021,144:104782. doi: 10.1016/j.ijrmms.2021.104782
[47] LISJAK A,YOUNG-SCHULTZ T,LI B,et al. A novel rockbolt formulation for a GPU-accelerated, finite-discrete element method code and its application to underground excavations[J]. International Journal of Rock Mechanics and Mining Sciences,2020,134:104410. doi: 10.1016/j.ijrmms.2020.104410
[48] 刘泉声,邓鹏海,毕 晨,等. 深部巷道软弱围岩破裂碎胀过程及锚喷−注浆加固FDEM数值模拟[J]. 岩土力学,2019,40(10):4065−4083. LIU Quansheng,DENG Penghai,BI Chen,et al. FDEM numerical simulation of the fracture and extraction process of soft surrounding rock mass and its rockbolt-shotcrete-grouting reinforcement methods in the deep tunnel[J]. Rock and Soil Mechanics,2019,40(10):4065−4083.
[49] HUANG Xing, LIU Quansheng, XU Xianze, et al. Lidar-based Convergence Deformation Monitoring System for Surrounding Rock around TBM Shield Region [P]. US: 16556246, 2022.08. 24.
[50] HUANG Xing,LIU Quansheng,LIU He,et al. Development and in-situ application of a real-time monitoring system for the interaction between TBM and surrounding rock[J]. Tunnelling and Underground Space Technology,2018,81:187−208. doi: 10.1016/j.tust.2018.07.018
[51] 刘泉声, 高 玮, 袁 亮. 煤矿深部岩巷稳定控制理论与支护技术及应用[M]. 北京: 科学出版社, 2010. [52] 张 娜,李建斌,荆留杰,等. 基于隧道掘进机掘进过程的岩体状态感知方法[J]. 浙江大学学报(工学版),2019,53(10):1−9. ZHANG Na,LI Jianbin,JING Liujie. Prediction method of rockmass parameters based on tunnelling process of tunnel boring machine[J]. Journal of Zhejiang University,2019,53(10):1−9.
[53] YAGIZ S. Development of rock fracture and brittleness indices to quantify the effects of rock mass features and toughness in the CSM model basic penetration for hard rock tunneling machines [D]. Golden: Colorado School of Mines, 2002.
[54] 罗 华. 基于线性回归和深度置信网络的TBM性能预测研究[D]. 杭州: 浙江大学, 2018. LUO HUA. Application of linear regression analysis and deep belief network for performance prediction of TBM [D]. Hangzhou: Zhejiang University, 2018.
[55] Mohammadreza K,Ahmad F,Ebrahim N G,et al. Development of a new hybrid ANN for solving a geotechnical problem related to tunnel boring machine performance[J]. Engineering with Computers,2020,36:345−357. doi: 10.1007/s00366-019-00701-8
[56] HUANG Xing,WANG Shaohua,LIU Quansheng,et al. Development of a real-time monitoring and calculation method for TBM disc-cutter’s cutting force in complex ground[J]. Geotechnical Testing Journal,2022,45(5):961−984.
[57] HUANG Xing, LIU Quansheng, HUANG Tao, et al. Method and apparatus for monitoring the interaction between surrounding rocks and TBM in TBM tunneling process [P]. US: 16/508, 316, 2022−03−15.
[58] LIU Quansheng,WANG Xinyu,HUANG Xing,et al. Prediction model of rock mass class using classification and regression tree integrated AdaBoost algorithm based on TBM driving data[J]. Tunnelling and Underground Space Technology,2020,106:103595. doi: 10.1016/j.tust.2020.103595
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