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Journal of ZheJiang University (Engineering Science)  2019, Vol. 53 Issue (10): 1977-1985    DOI: 10.3785/j.issn.1008-973X.2019.10.015
Civil Engineering     
Prediction method of rockmass parameters based on tunnelling process of tunnel boring machine
Na ZHANG1(),Jian-bin LI2,*(),Liu-jie JING1,3,Chen YANG1,Shuai CHEN1
1. China Railway Engineering Equipment Group Limited Company, Zhengzhou 450016, China
2. China Railway Hi-Tech Industry Corporation Limited, Beijing 400000, China
3. State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, China
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Abstract  

A prediction method of rockmass parameters was proposed based on tunnelling process of tunnel boring machine (TBM) in order to solve the problem that the rockmass parameters of tunnel face was obtained difficultly in real time during TBM construction. The variation of tunnelling parameters in the TBM construction process was analyzed supported by water supply project from Songhua River in Jilin Province. A database was established which included the tunnelling parameters of TBM and rockmass parameters, and the correlation between the rockmass parameters and the tunnelling parameters of TBM was deduced. A new relational model between rockmass and TBM was constructed based on the stepwise regression algorithm and the clustering algorithm. The uniaxial compressive strength (UCS) of rock, volumetric joint count and surrounding rockmass classification were predicted by monitoring the tunnelling parameters of TBM. The rock parameters of tunnel face in the limestone and granite strata were predicted and compared with the actual values. Results indicated that the predicted UCS and volumetric joint count had an estimated maximum error of 18%, and the evaluated comprehensively surrounding rockmass classification accorded well with the actual state of rockmass, which verified the accuracy of the research results.



Key wordstunnel boring machine (TBM)      tunnelling parameter      rockmass parameter      relational model between rock and tunnel boring machine      prediction method of rockmass parameter     
Received: 18 July 2018      Published: 30 September 2019
CLC:  U 45  
Corresponding Authors: Jian-bin LI     E-mail: znazna@163.com;lijianbin@crectbm.com
Cite this article:

Na ZHANG,Jian-bin LI,Liu-jie JING,Chen YANG,Shuai CHEN. Prediction method of rockmass parameters based on tunnelling process of tunnel boring machine. Journal of ZheJiang University (Engineering Science), 2019, 53(10): 1977-1985.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.10.015     OR     http://www.zjujournals.com/eng/Y2019/V53/I10/1977


基于隧道掘进机掘进过程的岩体状态感知方法

针对现有技术无法预先、实时获取隧道掘进机(TBM)掌子面岩体状态参数的问题,提出基于TBM掘进过程监测的岩体状态感知方法. 以吉林引松供水工程TBM施工隧道为依托,分析TBM掘进过程中掘进参数的变化规律,建立TBM掘进参数与岩体参数数据库,研究TBM设备参数和在掘岩体参数之间的相互关系. 分别采用分步回归和聚类分析的方法建立岩机关系模型,利用监测TBM掘进参数实时感知岩石强度、体积节理数和围岩等级等参数. 以石灰岩和花岗岩地层为例,对TBM在掘岩体参数的预测值与实际值进行对比. 结果表明,利用提出的岩体状态感知方法预测的岩石抗压强度UCS和体积节理数与实际值的误差小于18%,预测当前围岩等级与实际岩体状态基本一致,验证了研究结果的准确性.


关键词: 隧道掘进机(TBM),  掘进参数,  岩体参数,  岩机关系模型,  岩体感知方法 
Fig.1 Geological profile of water supply project from Songhua River
Fig.2 Monitoring interface of TBM hybrid cloud platform
Fig.3 Variations of thrust and torque and penetration of TBM in tunneling
Fig.4 Stage division of tunneling cycle of TBM
Fig.5 Fitting relation of thrust per cutter and penetration of TBM
数据 UCS/MPa Jv/(条·m?3 围岩等级 H/m P/mm Fn/kN Ta/(kN·m) V/(m·h?1 Rs/(r·min?1
最小值 38 3.8 V 30 7.3 116 1 554 2.73 1.5
最大值 95 25.7 II 236 15 326 3 469 5.31 7.2
平均值 57 11.4 ? 105 11.2 221 2 616 3.92 6.1
标准差 13 5.9 ? 50 1.6 54 466 0.52 0.6
Tab.1 Descriptive statistics of TBM and rockmass variables
a 值分布区间 频次 b 值分布区间 频次
0~5 8 60~100 10
5~10 16 100~140 26
10~15 19 140~180 7
15~20 3 180~200 3
Tab.2 Distribution of influence coefficient of penetration on thrust per cutter and rockmass breaking threshold
Fig.6 Forces acting on disc cutter
Fig.7 Distribution of FPI and TPI for different surrounding rockmass classification
里程 P/mm Fn/
kN
Ta/
(kN·m)
UCSa/
MPa
Jva/
(条·m?3
实际围岩
等级
70 961 8.96 293 2 759.1 53 11 III
70 716 10.1 303 3 075.9 95 9 III
69 085 9.4 278 2 544.1 56 7.8 III
68 717 11 210 2 946.5 58 12 IV
68 388 13 152 1 920.6 43 19 IV
66 154 13.2 222 2 873.5 76 5.4 V
63 124 8 256 2 781.6 66 5 IIIa
60 171 12 259 2 853.0 42 18 IIIb
Tab.3 Parameters of TBM and rockmass in limestone strata
里程 a b UCSc/
MPa
Jvc/
(条·m?3
BQ围岩分级
70 961 8.05 121.41 57.83 12.76 III
70 716 9.60 194.21 112.23 10.47 III
69 085 11.35 121.70 52.29 8.23 III
68 717 8.41 127.18 61.62 12.20 IV
68 388 5.20 87.05 39.30 18.62 IV
66 154 13.10 165.03 84.19 6.25 IV
63 124 14.36 150.04 70.94 4.94 III
60 171 5.13 85.09 38.07 18.81 IV
Tab.4 Prediction of rockmass parameters by stepwise regression algorithm in limestone strata
里程 FPI/
(kN·mm?1
TPI/
(kN·mm?1
聚类法围岩等级 BQ围岩
分级
实际围岩等级
70 961 32.70 2.38 III III III
70 716 30.00 2.35 III III III
69 085 29.57 2.09 III III III
68 717 19.09 2.07 IV IV IV
68 388 11.69 1.14 V IV IV
66 154 16.82 1.68 V IV V
63 124 32.00 2.68 III III IIIa
60 171 21.58 1.83 IV IV IIIb
Tab.5 Prediction of rockmass parameters by clustering algorithm in limestone strata
数据 UCS/MPa Jv/(条·m?3 围岩等级 P/mm Fn/kN Ta/(kN·m) Rs/(r·min?1
最小值 67.39 0 IV 1.35 151.85 1 571 8.52
最大值 236.51 29.3 I 4.53 227.42 3 966 9.70
平均值 167.24 7.2 ? 2.60 206.24 2 792 9.46
标准差 33.50 6.8 ? 0.85 19.00 646 0.30
Tab.6 Parameters of TBM and rockmass in granite strata
环号 UCSa/MPa Jva/
(条·m?3
围岩等级 a b UCSc/MPa Jvc/
(条·m?3
FPI/
(kN·mm?1
TPI/
(kN·mm?1
聚类法围
岩等级
BQ围岩
分级
实际围岩
等级
S2003 228.95 6.7 II 13.20 183.21 200.86 5.86 79.24 18.84 II II II
S2025 236.79 26.7 IV 9.47 150.51 269.14 22.71 52.53 13.22 IV IV IV
S2052 176.86 0 I 15.29 200.71 197.21 0 121.08 16.92 I I I
S2104 177.04 4.5 II 13.78 187.35 195.62 3.96 95.63 16.47 II I II
N1649 147.02 14 III 11.45 147.14 132.49 12.41 62.76 13.87 III III III
N1690 240.3 5.3 II 13.16 188.83 224.76 5.97 101.99 18.55 II II II
Tab.7 Prediction of rockmass parameters by stepwise regression algorithm in granite strata
Fig.8 Relational model for parameters of rockmass and TBM by clustering algorithm in granite strata
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