基于集成CART算法的TBM掘进参数与围岩等级预测

doi:10.13722/j.cnki.jrme.2019.0924

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

Download: PDF (3158 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract There are numerous data available to reflect the geological conditions during the operation period of full-section tunnel boring machine(TBM). The aims of this paper are to measure information of rockmasses in time and to intelligently optimize the tunneling parameters based on TBM operating data and CART-based ensemble learning algorithms，i.e. random forest and AdaCost. In order to establish models for rapidly and accurately predicting TBM tunneling parameters and surrounding rockmass conditions，a new pattern recognition method is proposed to divide a TBM tunneling cycle into empty pushing section，ascending section and stable section. Random forest model and the first 30 seconds data of ascending section are used to predict the values of the thrust and cutterhead torque during stable section，with the accuracy of 0.90 and 0.87 respectively. Moreover，a cost-sensitive AdaCost algorithm is used to predict the surrounding rockmass conditions with 16% and 50% precision improvements of grades IV and V compared to the random forest model，which solves the problem that the traditional machine learning algorithms are not applicable to imbalanced database. TBM operating parameters such as thrust，cutterhead power，cutterhead torque and advance rate are proved to be closely related to TBM tunneling，while cutterhead rotational speed，boot pressure，boot angle of pitch，advance rate and boot roll position and so on are shown to better reflect the surrounding rockmass conditions. These results are of great significance to TBM tunneling parameters optimizing and risk warning and will provide a reference to establish a data-based TBM intelligent decision and control platform in the future.

Key words： rock mechanics TBM tunnelling parameters surrounding rockmass conditions imbalanced database random forest AdaCost algorithm

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHU Mengqi1
	ZHU Hehua1
	2
	WANG Xin1
	CHENG Panpan1

Cite this article:

ZHU Mengqi1,ZHU Hehua1,2, et al. Study on CART-based ensemble learning algorithms for predicting TBM tunneling parameters and classing surrounding rockmasses#br#[J]. , 2020, 39(9): 1860-1871.

URL:

https://rockmech.whrsm.ac.cn/EN/10.13722/j.cnki.jrme.2019.0924 OR https://rockmech.whrsm.ac.cn/EN/Y2020/V39/I9/1860

[1] ZHU H H，YAN J X，LIANG W H. Challenges and development prospects of ultra-long and ultra-deep mountain tunnels[J]. Engineering，2019，5(3)：384–392.
[2] SUN W，SHI M L，ZHANG C，et al. Dynamic load prediction of tunnel boring machine (TBM) based on heterogeneous in-situ data[J]. Automation in Construction，2018，92(15)：23–34.
[3] OKUBO S，FUKUI K，CHEN W，Expert system for applicability of tunnel boring machines in Japan[J]. Rock Mechanics and Rock Engineering，2003，36(4)：305–322.
[4] JUNG J H，CHUNG H，KWON Y S，et al. An ANN to predict ground condition ahead of tunnel face using TBM operational data[J]. KSCE Journal of Civil Engineering，2019，23(7)：3 200–3 206.
[5] JING L J，LI J B，YANG C，et al. A case study of TBM performance prediction using field tunnelling tests in limestone strata[J]. Tunnelling and Underground Space Technology，2019，83(1)：364–372.
[6] FRID V. Rockburst hazard forecast by electromagnetic radiation excited by rock fracture[J]. Rock Mechanics and Rock Engineering，1997，30(4)：229–236.
[7] WANG J A，PARK H D. Comprehensive prediction of rockburst based on analysis of strain energy in rocks[J]. Tunnelling and Underground Space Technology，2001，16(1)：49–57.
[8] CHUNG H Y，LEE I M，JUNG J H，et al. Bayesian networks-based shield TBM risk management system：methodology development and application[J]. KSCE Journal of Civil Engineering，2019，23(1)：452–465.
[9] KIM J I，FISCHER M，KAM C. Generation and evaluation of excavation schedules for hard rock tunnels in preconstruction and construction[J]. Automation in Construction，2018，96(12)：378–397.
[10] LI S C，LIU B，XU X J，et al. An overview of ahead geological prospecting in tunneling[J]. Tunnelling and Underground Space Technology，2017，63(3)：69–94.
[11] LI S C，LI S C，ZHANG Q S，et al. Predicting geological hazards during tunnel construction[J]. Journal of Rock Mechanics and Geotechnical Engineering，2010，2(3)：232–242.
[12] SHI S S，LI S S，LI L P，et al. Advance optimized classification and application of surrounding rock based on fuzzy analytic hierarchy process and Tunnel Seismic Prediction[J]. Automation in construction，2014，37(1)：217–222.
[13] HU M，ZHOU F，ZHANG Y，et al. Acoustic emission monitoring on damage evolution of surrounding rock during headrace tunnel excavation by TBM[J]. European Journal of Environmental and Civil Engineering，2019，23(10)：1 248–1 264.
[14] 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(4)：73–83.
[15] ERHARTER G H，MARCHER T，REINHOLD C. Artificial neural network based online rockmass behavior classification of TBM data[C]// Proceedings of the 3rd International Conference (ICITG). [S. l. ]：[s. n.]，2019：178–188.
[16] SUN W，SHI M L，ZHANG C，et al. Dynamic load prediction of tunnel boring machine(TBM) based on heterogeneous in-situ data[J]. Automation in Construction，2018，92(8)：23–34.
[17] GALENDE-HERNANDEZ M，MENENDEZ M，FUENTE M J，et al. Monitorwhile-drilling-based estimation of rock mass rating with computational intelligence：the case of tunnel excavation front[J]. Automation in Construction，2018，93：325–338.
[18] YAMAMOTO T，SHIRASAGI S，YAMAMOTO S，et al. Evaluation of the geological condition ahead of the tunnel face by geostatistical techniques using TBM driving data[J]. Tunnelling and Underground Space Technology，2003，18(2)：213–221.
[19] 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.
[20] 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.
[21] ROSTAMI J，OZDEMIR L. A new model for performance prediction of hard rock TBMs[C]// Proceedings of the Rapid Excavation and Tunneling Conference. [S. l.]：[s. n.]，1993：793–809.
[22] 顾健健. 地应力下TBM盘形滚刀载荷特性及破岩参数匹配研究[硕士学位论文][D]. 长沙：中南大学，2013.(GU Jianjian. Mechanical characteristics of TBM disc cutter under the crustal stress and the parameters matching researches of rock-breaking[M. S. Thesis][D]. Changsha：Central south University，2013.(in Chinese))
[23] 周思阳，亢一澜，苏翠霞，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 requirement for TBMs based on mechanical analysis[J]. Journal of Mechanical Engineering，2016，52(20)：76–82.(in Chinese))
[24] 夏毅敏，朱湘衡，林赉贶，等. TBM刀盘掘进载荷影响因素研究[J]. 现代制造工程，2015，(9)：1–6.(XIA Yimin，ZHU Xiangheng，LIN Laikuang，et al. Analysis of influence factors on load of TBM cutterhead[J]. Modern Manufacturing Engineering，2015，(9)：1–6.(in Chinese))
[25] 韩美东. 全断面岩石掘进机刀盘掘进载荷特性与结构性能研究[博士学位论文][D]. 天津：天津大学，2017.(HAN Meidong. Study on tunneling loads and structural performance of cutterhead of full face rock tunnel boring machine[Ph. D. Thesis][D]. Tianjin：Tianjin University，2017.(in Chinese))
[26] 王俊，胡瑞青，周晓军. 中砂层地层TBM掘进三维离散元研究[J]. 施工技术，2016，45(22)：19–21.(WANG Jun，HU Ruiqing，ZHOU Xiaojun. Study on the TBM tunneling in medium sandstone using 3D DEM[J]. Construction Technology，2016，45(22)：19–21.(in Chinese)).
[27] ROSTAMI J. Study of pressure distribution within the crushed zone in the contact area between rock and disc cutters[J]. International Journal of Rock Mechanics and Mining Sciences，2013，57：172–186.
[28] 王超，龚国芳，杨华勇，等. NSVR硬岩隧道掘进机刀盘扭矩预测分析[J]. 浙江大学学报：工学版，2018，52(3)：479–486.(WANG Chao，GONG Guofang，YANG Huayong，et al. NSVR based predictive analysis of cutterhead torque for hard rock TBM[J]. Journal of Zhejiang University：Engineering Science，2018，52(3)：479–486.(in Chinese))
[29] SALIMI A，ROSTAMI J，MOORMANN C，et al.，Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs[J]. Tunneling and Underground Space Technology，2016，58(2)：236–246.
[30] GAO X J，SHI M L，SONG X G，et al. Recurrent neural networks for real-time prediction of TBM operating parameters[J]. Automation in Construction，2018，98：225–235.
[31] 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)：141–149.(LOU 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)：141–149.(in Chinese))
[32] 张娜，李建斌，荆留杰，等. TBM掘进参数智能控制系统的研究与应用[J]. 隧道建设，2018，38(10)：150–156.(ZHANG Na，LI Jianbin，JING Liujie，et al. Study and application of intelligent control system of TBM tunneling parameters[J]. Tunnel Construction，2018，38(10)：150–156.(in Chinese))
[33] QUINLAN J R. Induction on decision tree[J]. Machine Learning，1986，1(1)：81–106.
[34] QUINLAN J R. Probabilistic decision trees[J]. Machine Learning，1990，3：140–152.
[35] WYNER A D. Shannon-theoretic approach to a Gaussian cellular multiple-access channel[J]. IEEE Transactions on Information Theory，1994，40(6)：1 713–1 727.
[36] ABELLAN J，MANTAS C J，CASTELLANO J G. A random forest approach using imprecise probabilities[J]. Knowledge-Based Systems，2017，134：72–84.
[37] BREIMAN L. Random forests[J]. Machine Learning，2001，45(1)：5–32.
[38] RUDZIANSKAITE K，APANAVICIENE R R，GELZINIS A. Modelling the effectiveness of PPP road infrastructure projects by applying random forests[J]. Journal of Civil Engineering and Management，2015，21：290–299.
[39] YU B，WANG H Z，SHAN W X，et al. Prediction of bus travel time using random forests based on near neighbors[J]. Computer-Aided Civil and Infrastructure Engineering，2018，33(4)：333–-350.
[40] ZHOU J，SHI X Z，DU K，et al. Feasibility of random-forest approach for prediction of ground settlements induced by the construction of a shield-driven tunnel[J]. International Journal of Geomechanics，2017，17(6)：1–12.
[41] NOJIMA Y，MIHARA S，ISHIBUCHI H. Application of parallel distributed genetics-based machine learning to imbalanced data sets[C]// 2012 IEEE International Conference on Fuzzy Systems. [S. l.]：[s. n.]，2012：1–6.
[42] EVERITT B S. Classification and regression trees[J]. Encyclopedia of Statistics in Behavioral Science，2005，1：287–290.
[43] SELIYA N，KHOSHGOFTAAR T M，The use of decision trees for cost-sensitive classification：an empirical study in software quality prediction[J]. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery，2011，1(5)：448–459.
[44] KOHAVI R，A study of cross-validation and bootstrap for accuracy estimation and model selection[C]// Proceedings of the 14th International Joint Conference on Artificial Intelligence. [S. l.]：[s. n.]，1995：1 137–1 145.
[45] 曹正凤. 随机森林算法优化研究[博士学位论文][D]. 北京：首都经济贸易大学，2014.(CAO Zhengfeng. Study on optimization of random forests algorithm[Ph. D. Thesis][D]. Beijing：Capital University of Economics and Business，2014.(in Chinese))
[46] 中华人民共和国国家标准编写组. GB50487—2008 水利水电工程地质勘察规范[S]. [S. l]：[s. n.]，2008.(The National Standards Compilation Group of the People's Republic of China. GB50487—2008 Code for Engineering geological investigation of water resources and hydropower[S]. [S. l]：[s. n.]，2008.(in Chinese))