A method for classification of surrounding rock based on the excavatability performance and adaptability of tunnel boring machines and its applications

Authors

LIU Jiawei, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454001, ChinaFollow
ZHANG Sheng, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454001, China; Collaborative Innovation Center of Coal Work Safety and Clean High Efficiency Utilization, Jiaozuo 454001, ChinaFollow
CHEN Zhao, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454001, China
YANG Zhanbiao, Coal Mining and Utilization Research Institute, Pingdingshan Tianan Coal Mining Co., Ltd., Pingdingshan 467099, China
JI Panjun, Henan Pingbao Coal Co., Ltd., Xuchang 461714, China
WEI Yonghui, School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo 454001, China

Abstract

The varying geological conditions of the surrounding rocks of coal mine roadways influence the wide application of full-face rock tunnel boring machines (TBMs). Therefore, accurately assessing the excavatability of rock masses in coal mines and the rock adaptability of TBMs is crucial for the efficient operation of TBMs. Based on the evaluation results of the parameters and excavatability of rock masses, this study built a classification model for rock mass excavatability using the technique for order preference by similarity to ideal solution (TOPSIS). Furthermore, combining the correlation analysis between the [BQ] values of different geological conditions and the utilization ratio of TBMs, this study proposed a classification model for TBM adaptability to rock layers. Moreover, this study evaluated the rock mass excavatability and the rock adaptability of TBMs based on the daily advance rate. Accordingly, it established a comprehensive classification method for surrounding rocks based on TBM performance. As indicated by the field application of this classification method based on the engineering data from the TBM excavation of a gas drainage roadway on the floor in Shoushan Mine No.1, the average monthly footage of the roadway achieved using a TBM could reach 400 m under a rock mass excavatability of Grade I and a rock adaptability of Grade 3. When the utilization ratio of TBM was less than 20%, on-site problems, such as TBM jamming and difficulties with slag discharge, were highly liable to occur. Based on the automatically collected TBM excavation data and the analysis results of surrounding rock properties, the comprehensive classification method for surrounding rocks proposed in this study can dynamically evaluate the performance of TBMs under different geological conditions of surrounding rocks, thus providing a theoretical basis for the design of the tunning control parameters of TBMs.

Keywords

Tunnel Boring Machine (TBM), coal mine roadway, classification of surrounding rocks, excavatability, adaptability to rock layers

DOI

10.12363/issn.1001-1986.23.05.0290

Recommended Citation

LIU Jiawei, ZHANG Sheng, CHEN Zhao, et al. (2023) "A method for classification of surrounding rock based on the excavatability performance and adaptability of tunnel boring machines and its applications," Coal Geology & Exploration: Vol. 51: Iss. 8, Article 18.
DOI: 10.12363/issn.1001-1986.23.05.0290
Available at: https://cge.researchcommons.org/journal/vol51/iss8/18

Reference

[1] 刘泉声,黄兴,潘玉丛,等. TBM在煤矿巷道掘进的技术应用和研究进展[J]. 煤炭科学技术,2023,51(1):242−260.

LIU Quansheng,HUANG Xing,PAN Yucong,et al. Application and research progress of TBM tunneling in coal mine roadway[J]. Coal Science and Technology,2023,51(1):242−260.

[2] 荆留杰. TBM掘进性能预测及智能辅助控制研究[D]. 徐州：中国矿业大学，2022.

JING Liujie. Research on TBM tunneling performance predication and intelligent auxiliary control[D]. Xuzhou：China University of Mining and Technology，2022.

[3] 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.

[4] 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.

[5] 贺飞，鲁义强，代恩虎，等. 煤矿岩巷TBM适应性与新技术发展[J/OL]. 煤炭科学技术，2023：1–13 [2023-08-01]. https://doi.org/10.13199/j.cnki.cst.2022–1404.

HE Fei，LU Yiqiang，DAI Enhu，et al. Application of TBM in coal mine adaptability type selection analysis and new technology development[J/OL]. Coal Science and Technology，2023：1−13 [2023-08-01]. https://doi.org/10.13199/j.cnki.cst.2022–1404.

[6] 王杜娟,贺飞,王勇,等. 煤矿岩巷全断面掘进机(TBM)及智能化关键技术[J]. 煤炭学报,2020,45(6):2031−2044.

WANG Dujuan,HE Fei,WANG Yong,et al. Tunnel boring machine (TBM) in coal mine and its intelligent key technology[J]. Journal of China Coal Society,2020,45(6):2031−2044.

[7] 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.

[8] 王旭,李晓,李守定. 关于用岩体分类预测TBM掘进速率AR的讨论[J]. 工程地质学报,2008,16(4):470−475.

WANG Xu,LI Xiao,LI Shouding. Problems in the prediction of the TBM advance rate with rock mass classification and their possible solutions[J]. Journal of Engineering Geology,2008,16(4):470−475.

[9] 王国法. 煤矿智能化最新技术进展与问题探讨[J]. 煤炭科学技术,2022,50(1):1−27.

WANG Guofa. New technological progress of coal mine intelligence and its problems[J]. Coal Science and Technology,2022,50(1):1−27.

[10] RIBACCHI R,FAZIO A L. Influence of rock mass parameters on the performance of a TBM in a Gneissic Formation (Varzo Tunnel)[J]. Rock Mechanics and Rock Engineering,2005,38(2):105−127.

[11] ROSTAMI J I. Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure[D]. USA Colarado：Colorado School of Mines，1997.

[12] SANIO H P. Prediction of the performance of disc cutters in anisotropic rock[J]. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts,1985,22(3):153−161.

[13] 周思阳,亢一澜,苏翠侠,等. 基于力学分析的TBM掘进总推力预测模型研究[J]. 机械工程学报,2016,52(20):76−82.

ZHOU Siyang,KANG Yilan,SU Cuixia,et al. Prediction of thrust force requirements for TBMs based on mechanical analysis[J]. Journal of Mechanical Engineering,2016,52(20):76−82.

[14] BIENIAWSKI Z T，CELADA B，GALERA J M，et al. New applications of the excavability index for selection of TBM types and predicting their performance[C]//ITA–AITES World Tunnel Congress and 34^th ITA General Assembly. Agra，2008：1618–1629.

[15] BARTON N,LIEN R,LUNDE J. Engineering classification of rock masses for the design of tunnel support[J]. Rock Mechanics,1974,6(4):189−236.

[16] 杜立杰,齐志冲,韩小亮,等. 基于现场数据的TBM可掘性和掘进性能预测方法[J]. 煤炭学报,2015,40(6):1284−1289.

DU Lijie,QI Zhichong,HAN Xiaoliang,et al. Prediction method for the boreability and performance of hard rock TBM based on boring data on site[J]. Journal of China Coal Society,2015,40(6):1284−1289.

[17] 龚秋明,卢建炜,魏军政,等. 基于岩体分级系统(RMR)评估预测TBM利用率研究[J]. 施工技术,2018,47(5):92−98.

GONG Qiuming,LU Jianwei,WEI Junzheng,et al. Study on estimation and prediction of TBM utilization rate using rock mass rating (RMR)[J]. Construction Technology,2018,47(5):92−98.

[18] 薛亚东,李兴,刁振兴,等. 基于掘进性能的TBM施工围岩综合分级方法[J]. 岩石力学与工程学报,2018,37(增刊1):3382−3391.

XUE Yadong,LI Xing,DIAO Zhenxing,et al. A novel classification method of rock mass for TBM tunnel based on penetration performance[J]. Chinese Journal of Rock Mechanics and Engineering,2018,37(Sup.1):3382−3391.

[19] 王胜乐. 引汉济渭TBM施工隧洞围岩分类方法研究及应用[D]. 西安：西安理工大学，2021.

WANG Shengle. Study and application of surrounding rock classification method for TBM tunnel construction of Hanjiang–Weihe River diversion project[D]. Xi’an：Xi’an University of Technology，2021.

[20] 吴鑫林. TBM施工隧洞围岩质量分级研究[D]. 武汉：武汉大学，2020.

WU Xinlin. Research on the classification of surrounding rock quality for TBM excavated tunnel[D]. Wuhan：Wuhan University，2020.

[21] 王攀. 硬岩掘进机可掘进性预测分析方法研究[D]. 天津：天津大学，2014.

WANG Pan. Research on penetrability prediction analysis method of tunneling boring machine[D]. Tianjin：Tianjin University，2014.

[22] 吴鑫林,张晓平,刘泉声,等. TBM岩体可掘性预测及其分级研究[J]. 岩土力学,2020,41(5):1721−1729.

WU Xinlin,ZHANG Xiaoping,LIU Quansheng,et al. Prediction and classification of rock mass boreability in TBM tunnel[J]. Rock and Soil Mechanics,2020,41(5):1721−1729.

[23] ROSTAMI J I，OZDEMIR L. New model for performance production of hard rock TBMs[C]//Proceedings of the Rapid Excavation and Tunneling Conference，June 13−17，1993. Boston，1993.

[24] 司富安,李坤,段世委. TBM施工深埋水工长隧洞围岩综合分类研究[J]. 长江科学院院报,2020,37(8):150−154.

SI Fuan,LI Kun,DUAN Shiwei. Comprehensive classification of surrounding rock of deep buried long hydraulic tunnel constructed with tunnel boring machine[J]. Journal of Yangtze River Scientific Research Institute,2020,37(8):150−154.

[25] GONG Qiuming,LU Jianwei,XU Hongyi,et al. A modified rock mass classification system for TBM tunnels and tunneling based on the HC method of China[J]. International Journal of Rock Mechanics and Mining Sciences,2021,137:104551.

[26] JI Feng,SHI Yuchuan,LI Renjie,et al. Modified Q–index for prediction of rock mass quality around a tunnel excavated with a tunnel boring machine (TBM)[J]. Bulletin of Engineering Geology and the Environment,2019,78(5):3755−3766.

[27] 何发亮,谷明成,王石春. TBM施工隧道围岩分级方法研究[J]. 岩石力学与工程学报,2002,21(9):1350−1354.

HE Faliang,GU Mingcheng,WANG Shichun. Study on surrounding rockmass classification of tunnel cut by TBM[J]. Chinese Journal of Rock Mechanics and Engineering,2002,21(9):1350−1354.

[28] LIU Quansheng,LIU Jianping,PAN Yucong,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.

[29] 中华人民共和国住房和城乡建设部，中华人民共和国国家质量监督检验检疫总局. 工程岩体分级标准：GB/T 50218—2014[S]. 北京：中国计划出版社，2015.

[30] FARROKH E,ROSTAMI J,LAUGHTON C. Analysis of unit supporting time and support installation time for open TBMs[J]. Rock Mechanics and Rock Engineering,2011,44(4):431−445.

[31] BEHZADIAN M,OTAGHSARA S K,YAZDANI M,et al. A state–of the–art survey of TOPSIS applications[J]. Expert Systems with Applications,2012,39(17):13051−13069.

[32] 吴志军,方立群,翁磊,等. 基于TBM掘进性能的岩体分级及可掘性等级感知识别方法[J]. 岩石力学与工程学报,2022,41(增刊1):2684−2699.

WU Zhijun,FANG Liqun,WENG Lei,et al. A classification and boreability perception and recognition method for rock mass based on TBM tunneling performance[J]. Chinese Journal of Rock Mechanics and Engineering,2022,41(Sup.1):2684−2699.

[33] 朱梦琦,朱合华,王昕,等. 基于集成CART算法的TBM掘进参数与围岩等级预测[J]. 岩石力学与工程学报,2020,39(9):1860−1871.

ZHU Mengqi,ZHU Hehua,WANG Xin,et al. Study on CART–based ensemble learning algorithms for predicting TBM tunneling parameters and classing surrounding rockmasses[J]. Chinese Journal of Rock Mechanics and Engineering,2020,39(9):1860−1871.

[34] 罗华,陈祖煜,龚国芳,等. 基于现场数据的TBM掘进速率研究[J]. 浙江大学学报(工学版),2018,52(8):1566−1574.

LUO Hua,CHEN Zuyu,GONG Guofang,et al. Advance rate of TBM based on field boring data[J]. Journal of Zhejiang University (Engineering Science),2018,52(8):1566−1574.

[35] 刘泉声,刘建平,潘玉丛,等. 硬岩隧道掘进机性能预测模型研究进展[J]. 岩石力学与工程学报,2016,35(增刊1):2766−2786.

LIU Quansheng,LIU Jianping,PAN Yucong,et al. Research advances of tunnel boring machine performance prediction models for hard rock[J]. Chinese Journal of Rock Mechanics and Engineering,2016,35(Sup.1):2766−2786.

[36] 陶宇帆. TBM岩–机交互机制及控制参数分类约束优化方法研究[D]. 济南：山东大学，2021.

TAO Yufan. Research on TBM rock–mechanical interaction mechanism and control optimization method based on classification constrain[D]. Jinan：Shandong University，2021.