基于TBM掘进性能的岩体分级及可掘性等级感知识别方法

doi:10.13722/j.cnki.jrme.2021.0386

Abstract
Figure/Table
References (0)
Related Citation (15)

Download: PDF (954 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract The traditional surrounding rock classification method is mainly based on the drill and blast method，which is not suitable for evaluating and predicting the TBM tunneling performance. Therefore，the purpose of this paper is to establish a rock mass boreability classification method suitable for TBM construction and achieve accurate perception and recognition of the boreability grade. For this purpose，based on the rock mass and TBM machine data sourced from four tunnels，the correlations between the rock mass parameters and TBM boreability evaluation indices were analyzed，and the multi-criteria decision-making(MCDM) method TOPSIS was used to establish the rock boreability classification system for TBM tunnel. Since it was difficult to obtain the parameters of rock mass in actual TBM excavation directly，the proposed rock mass boreability grade was recognized by using TBM driving parameters，including the thrust force per cutter(Th)，penetration rate(PR)，cutterhead revolutions per minutes(RPM) and penetration rate per revolution(PRev). In the perception and recognition，Bayesian optimization was used to determine the optimal combination of input parameters of various machine learning classification algorithms to achieve the maximum accuracy of grade recognition. The applicability of different optimized algorithms for the boreability grade recognition was compared，and then the optimal perception and recognition method was determined. Finally，based on the real-time TBM data of Jilin Yinsong tunnel project，the accuracy and effectiveness of the proposed classification，as well as the perception and recognition method of rock mass boreability were verified. The research results can be used to predict the boreability of rock mass in the stable operation sections of TBM projects，and provide reference for the optimization of TBM driving parameters and the establishment of intelligent control system.

Key words： tunnelling engineering TBM classification of rock mass Bayesian optimization perception and recognition

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	WU Zhijun1
	2
	3
	FANG Liqun1
	2
	WENG Lei1
	2
	LIU Quansheng1
	2
	3

Cite this article:

WU Zhijun1,2,3, et al. A classification and boreability perception and recognition method for rock mass based on TBM tunneling performance[J]. , 2022, 41(S1): 2684-2699.

URL:

https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2021.0386 OR https://rockmech.whrsm.ac.cn/EN/Y2022/V41/IS1/2684

[1]	LIU Q，HUANG X，GONG Q，et al. Application and development of hard rock TBM and its prospect in China[J]. Tunnelling and Underground Space Technology，2016，57：33-46.
[2]	GONG Q，YIN L，MA H，et al. TBM tunnelling under adverse geological conditions：An overview[J]. Tunnelling and Underground Space Technology，2016，57：4-17.
[3]	SALIMI A，ROSTAMI J，MOORMANN C. Application of rock mass classification systems for performance estimation of rock TBMs using regression tree and artificial intelligence algorithms[J]. Tunnelling and Underground Space Technology，2019，92：103046.
[4]	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.
[5]	GONG Q，LU J，XU H，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，2020，137(4)：104551.
[6]	JI F，SHI Y C，LI R J，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)：3 755-3 766.
[7]	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)：1 350-1 354.(HE Faliang，GU Mingcheng，WANG Shichun. Study on surrounding rock classification of tunnel cut by TBMs[J]. Chinese Journal of Rock Mechanics and Engineering，2002，21(9)：1 350-1 354.(in Chinese))
[8]	李春明，彭耀荣. TBM施工隧洞围岩分类方法的探讨[J]. 中外公路，2006，26(3)：235-237.(LI Chunming，PENG Yaorong. Discussion about surrounding rock classification of tunnel excavate by TBMs[J]. Journal of China and Foreign Highway，2006，26(3)：235-237.(in Chinese))
[9]	BIENIAWSKI Z T，CELADA B，GALERA J M. TBM Excavability：prediction and machine-rock interaction[C]// Proceedings of RETC. Toronto：[s. n.]，2007：1 118-1 130.
[10]	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1)：3 382-3 391. (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(Supp.1)：3 382-3 391.(in Chinese))
[11]	ROSTAMI J. Development of a force estimation model for rock fragmentation with disc cutters through theoretical modeling and physical measurement of crushed zone pressure[Ph. D. Thesis][D]. USA Colorado：Colorado School of Mines，1997.
[12]	BRULAND A. Hard rock tunnel boring[Ph. D. Thesis][D]. Norway Trondheim：Norwegian University of Science and Technology，2000.
[13]	YAGIZ S，KARAHAN H. Application of various optimization techniques and comparison of their performances for predicting TBM penetration rate in rock mass[J]. International Journal of Rock Mechanics and Mining Sciences，2015，80：308-315.
[14]	FARROKH E，ROSTAMI J，LAUGHTON C. Study of various models for estimation of penetration rate of hard rock TBMs[J]. Tunnelling and Underground Space Technology，2012，30：110-123.
[15]	GONG Q M，ZHAO J. Development of a rock mass characteristics model for TBM penetration rate prediction[J]. International Journal of Rock Mechanics and Mining Sciences，2009，46(1)：8-18.
[16]	HASSANPOUR J，ROSTAMI J，ZHAO J. A new hard rock TBM performance prediction model for project planning[J]. Tunnelling and Underground Space Technology，2011，26(5)：595-603.
[17]	MAHDEVARI S，SHAHRIAR K，YAGIZ S，et al. A support vector regression model for predicting tunnel boring machine penetration rates[J]. International Journal of Rock Mechanics and Mining Sciences，2014，72：214-229.
[18]	SALIMI A，ROSTAMI J，MOORMANN C. Evaluating the suitability of existing rock mass classification systems for TBM performance prediction by using a regression tree[J]. Procedia Engineering，2017，191：199-309.
[19]	KOOPIALIPOOR M，FAHIMIFAR A，GHALEINI E N，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(1)：345-357.
[20]	FENG S，CHEN Z，LUO H，et al. Tunnel boring machines(TBM) performance prediction：A case study using big data and deep learning[J]. Tunnelling and Underground Space Technology，2021，110：103636.
[21]	LIU B，WANG R，GUAN Z，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.
[22]	LIU B，WANG R，ZHAO G，et al. Prediction of rock mass parameters in the TBM tunnel based on BP neural network integrated simulated annealing algorithm[J]. Tunnelling and Underground Space Technology，2020，95：103103.
[23]	ZHANG Q，LIU Z，TAN J. Prediction of geological conditions for a tunnel boring machine using big operational data[J]. Automation in Construction，2019，100：73-83.
[24]	LIU Q，WANG X，HUANG X，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.
[25]	BEHAZADIAN 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)：13 051-13 069.
[26]	LAI Y J，LIU T Y，HWANG C L. Topsis for MODM[J]. European Journal of Operational Research，1994，76(3)：486-500.
[27]	XU C，LIU X，WANG E，et al. Prediction of tunnel boring machine operating parameters using various machine learning algorithms[J]. Tunnelling and Underground Space Technology，2021， 109：103699.
[28]	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)：1 860- 1 871.(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)：1 860- 1 871.(in Chinese))
[29]	WILLIAMS C K，RASMUSSEN C E. Gaussian processes for machine learning[M]. Cambridge，MA：MIT Press，2006：39-42.
[30]	SHAHRIARI B，SWERSKY K，WANG Z，et al. Taking the human out of the loop: A review of Bayesian optimization[J]. Proceedings of the IEEE，2015，104(1)：148-175.
[31]	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1)：2 766-2 786.(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(Supp.1)：2 766-2 786.(in Chinese))
[32]	HASSANPOUR J，ROSTAMI J，KHAMEHCHIYAN M，et al. TBM performance analysis in pyroclastic rocks：a case history of karaj water conveyance tunnel[J]. Rock Mechanics and Rock Engineering，2010，43(4)：427-445.
[33]	LIU Q，LIU J，PAN Y，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.
[34]	KHADEMI HAMIDI J，SHAHRIAR K，REZAI B，et al. Performance prediction of hard rock TBM using Rock Mass Rating(RMR) system[J]. Tunnelling and Underground Space Technology，2010，25(4)：333-345.
[35]	SALIMI A，ROSTAMI J，MOORMANN C，et al. Examining feasibility of developing a rock mass classification for hard rock TBM Application using non-linear regression，regression tree and generic programming[J]. Geotechnical and Geological Engineering，2018，36(2)：1 145-1 159.
[36]	XU X，WU Z，SUN H，et al. An extended numerical manifold method for simulation of grouting reinforcement in deep rock tunnels[J]. Tunnelling and Underground Space Technology，2021，115(2)：104020.
[37]	XUE Y，LI Z，QIU D，et al. Classification model for surrounding rock based on the PCA-ideal point method：an engineering application[J]. Bulletin of Engineering Geology and the Environment，2019，78(5)： 3 627-3 635.
[38]	YAGIZ S. Utilizing rock mass properties for predicting TBM performance in hard rock condition[J]. Tunnelling and Underground Space Technology，2008，23(3)：326-339.
[39]	GHASEMI E，YAGIZ S，ATAEI M. Predicting penetration rate of hard rock tunnel boring machine using fuzzy logic[J]. Bulletin of Engineering Geology and the Environment，2014，73(1)：23-35.
[40]	HASSANPOUR J，GHAEDI VANANI A A，ROSTAMI J，et al. Evaluation of common TBM performance prediction models based on field data from the second lot of Zagros water conveyance tunnel (ZWCT2)[J]. Tunnelling and Underground Space Technology，2016，52：147-156.
[41]	王健. 基于机器学习的TBM掘进性能预测与岩体参数表征方法研究[硕士学位论文][D]. 济南：山东大学，2017.(WANG Jian. Research on TBM performance prediction and rock mass parameters characterization method based on machine learning[M. S. Thesis][D]. Jinan：Shandong University，2017.(in Chinese))
[42]	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)：1 721-1 729.(WU Xinlin，ZHANG Xiaoping，LIU Quansheng，et al. The prediction and classification of rock mass boreability in TBM tunnel[J]. Rock and Soil Mechanics，2020，41(5)：1 721-1 729.(in Chinese))
[43]	ZHANG Q，HU W，LIU Z，et al. TBM performance prediction with Bayesian optimization and automated machine learning[J]. Tunnelling and Underground Space Technology，2020，103：103493.
[44]	PEDREGOSA F，VAROQUAUX G，GRAMFORT A，et al. Scikit-learn：Machine Learning in Python[J]. The Journal of Machine Learning Research，2012，12：2 825-2 830.