Please wait a minute...
浙江大学学报(工学版)
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
Download: HTML     PDF(1117KB) HTML
Export: BibTeX | EndNote (RIS)      

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 words： tunnel 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
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Na ZHANG
Jian-bin LI
Liu-jie JING
Chen YANG
Shuai CHEN
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.

URL:

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
[1]   杜彦良, 杜立杰. 全断面岩石隧道掘进机系统原理与集成设计[M]. 武汉: 华中科技大学出版社, 2010.
[2]   王梦恕, 李典璜, 张镜剑, 等. 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.
[3]   荆留杰, 张娜, 杨晨 TBM及其施工技术在中国的发展与趋势[J]. 隧道建设, 2016, 36 (3): 331- 337
JING Liu-jie, ZHANG Na, YANG Chen Development of TBM and its construction technologies in China[J]. Tunnel Construction, 2016, 36 (3): 331- 337
doi: 10.3973/j.issn.1672-741X.2016.03.014
[4]   卢瑾, 高捷, 梅稚平 岩石力学参数对TBM掘进速率影响的分析[J]. 水电能源科学, 2010, 28 (7): 44- 46
LU Jin, GAO Jie, MEI Zhi-ping Influence analysis of rock mechanical parameters on TBM penetrating rate[J]. Water Resources and Power, 2010, 28 (7): 44- 46
doi: 10.3969/j.issn.1000-7709.2010.07.013
[5]   马洪素, 纪洪广 节理倾向对TBM滚刀破岩模式及掘进速率影响的试验研究[J]. 岩石力学与工程学报, 2011, 30 (1): 155- 163
MA Hong-su, JI Hong-guang Experimental study of the effect of joint orientation on fragmentation modes and penetration rate under TBM disc cutters[J]. Chinese Journal of Rock Mechanics and Engineering, 2011, 30 (1): 155- 163
[6]   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): 2404- 2411
SHANG Yan-jun, YANG Zhi-fa, ZENG Qing-li, 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): 2404- 2411
doi: 10.3321/j.issn:1000-6915.2007.12.004
[7]   刘汉东, 姜彤, 黄志全, 等. 岩体力学参数优选理论及应用[M]. 郑州: 黄河水利出版社, 2006.
[8]   KAUS A, BOENING W BEAM-geoelectrical ahead monitoring for TBM-drives[J]. Geomechanics and Tunneling, 2008, 1 (5): 442- 450
doi: 10.1002/geot.v1:5
[9]   BORM G, GIESE R, OTTO P, et al. Integrated seismic imaging system for geological prediction during tunnel construction [C] // Proceedings of 10th ISRM Congress. [S. l.]: ISRM, 2003: 137-142.
[10]   JETSCHNY S, BOHLEN T, KURZMANN A Seismic prediction of geological structures ahead of the tunnel using tunnel surface waves[J]. Geophysical Prospecting, 2011, 59 (5): 934- 946
[11]   KNEIB G, KASSEL A, LORENZ K Automatic seismic prediction ahead of the tunnel boring machine[J]. First Break, 2000, 18 (7): 295- 302
doi: 10.1046/j.1365-2397.2000.00079.x
[12]   PETRONIO L, POLETTO F, SCHLEIFER A Interface prediction ahead of the excavation front by the tunnel-seismic-while-drilling (TSWD) method[J]. Geophysics, 2007, 72 (4): G39- G44
doi: 10.1190/1.2740712
[13]   POLETTO F, CORUBOLO P, COMELLI P Drill-bit seismic interferometry with and without pilot signals[J]. Geophysical Prospecting, 2010, 58 (2): 257- 265
doi: 10.1111/gpr.2010.58.issue-2
[14]   YUE Z Q, LEE C F, LAW K T, et al Use of HKU drilling process monitor in soil nailing in slope stabilization[J]. Chinese Journal of Rock Mechanics and Engineering, 2002, 21 (11): 1685- 1690
[15]   SUGAWAWA J, YUE Z Q, THAM L G, et al Weathered rock characterization using drilling parameters[J]. Canadian Geotechnical Journal, 2003, 40 (3): 661- 668
doi: 10.1139/t03-007
[16]   KAHRAMAN S Correlation of TBM and drilling machine performances with rock brittleness[J]. Engineering Geology, 2002, 65 (4): 269- 283
doi: 10.1016/S0013-7952(01)00137-5
[1] Yi-zhe MAO,Guo-fang GONG,Xing-hai ZHOU,Fei WANG. Identification of TBM surrounding rock based on Markov process and deep neural network[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(3): 448-454.
[2] Fei WANG,Guo-fang GONG,Li-wen DUAN,Yong-feng QIN. XGBoost based intelligent determination system design of tunnel boring machine operation parameters[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(4): 633-641.
[3] Yu-xi CHEN,Guo-fang GONG,Zhuo SHI,Hua-yong YANG. Coordinated control of gripper and thrust system for TBM based on construction data[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(2): 250-257.
[4] Yan-chao TIAN,Fei HE,Xiao ZHANG. Adaptive design of shield radius for open type hard rock TBM[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(12): 2280-2288.
[5] LIU Jian-qin, XING Zhen-hua, BIN Huai-cheng, GUO Wei. Cutters' layout method of tunnel boring machine cutter-head under mixed-face rock ground[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(1): 166-173.
[6] LIN Lai-kuang, XIA Yi-min, JIA Lian-hui, HE Fei, YANG Mei, YANG Kai. Influence of installation and tunnelling parameters on rock-breaking resistance of disc cutter[J]. Journal of ZheJiang University (Engineering Science), 2018, 52(6): 1209-1215.
[7] WANG Kai, YANG Yu-hu, HUANG Tian, NIU Wen-wen, HE Fei. Topological structure and kinematic characteristic analysis of TBM gripping-thrusting-regripping mechanism[J]. Journal of ZheJiang University (Engineering Science), 2017, 51(6): 1135-1142.
[8] LIU Tong, GONG Guo fang, PENG Zuo, WU Wei qiang, PENG Xiong bin. Hybrid cutterhead driving system for TBM based on hydraulic transformer[J]. Journal of ZheJiang University (Engineering Science), 2016, 50(3): 419-427.
[9] LIU Tong, GONG Guo fang, PENG Zuo, WU Wei qiang, PENG Xiong bin. Hybrid cutterhead driving system for TBM based on hydraulic transformer[J]. Journal of ZheJiang University (Engineering Science), 2016, 50(2): 0-.
[10] WANG Wei-ping, ZHANG Huai-liang, ZHANG Guo-liang.
Analysis of valve port leakage on direct operated relief valve under foundation vibration
[J]. Journal of ZheJiang University (Engineering Science), 2015, 49(6): 1160-1165.
[11] ZHANG Zhen, GONG Guo fang, WU Wei qiang, LIU Tong, RAO Yun yi, ZHOU Jian jun. Adaptive attitude control of thrust system for hard rock tunnel boring machine[J]. Journal of ZheJiang University (Engineering Science), 2015, 49(10): 1870-1877.