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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2021, Vol. 55 Issue (7): 1339-1350    DOI: 10.3785/j.issn.1008-973X.2021.07.013
    
Risk prediction of TBM jamming based on dynamic Bayesian network
Fang-di XIE(),Qiang ZHAI,Wei-hong GU*()
School of Civil Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
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Abstract  

A probability system of jamming was analyzed based on dynamic Bayesian network(BN), in order to predict the risk of tunnel boring machine (TBM) jamming during tunnel mechanical construction. Through expert knowledge and explanation structure model to determine the causal relationship between risk factors and risk events, collecting the measured data of domestic tunnel mechanical engineering geological conditions. The risk indicators were divided according to the relevant specifications, the research results and the cloud division method of cloud model into internal division. The rough set classification principle was applied to discretization of data to obtain the original prior probability of risk factors and the conditional probability of risk events. Combined GENIE software a dynamic BN model was established to predict the risk of card machines. Results show that the risk probability of TBM jamming is 8% under the condition of no evidence. The key risk factors of TBM jamming are rock type, large amount of groundwater and fracture zone. The key cause chain of TBM clamping machine is as follows: rock type → progradation of mud and sand on palm face → clamping knife plate → clamping machine, a large amount of groundwater → progradation of mud and sand on the palm surface → clamping knife plate → clamping machine, high ground stress → large deformation of soft rock → clamping shield → clamping machine, surrounding rock collapse → clamping shield → clamping machine.

Key words： tunnel boring machine (TBM)      risk prediction of jamming machine      Bayesian network(BN)      rough set      cloud model     
Received: 20 May 2020      Published: 05 July 2021
CLC:  TU 94  
Fund:  国家自然科学基金资助项目（51668037）
Corresponding Authors: Wei-hong GU     E-mail: 1220530047@qq.com;lzgwh@163.com
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Fang-di XIE
Qiang ZHAI
Wei-hong GU
Cite this article:

Fang-di XIE,Qiang ZHAI,Wei-hong GU. Risk prediction of TBM jamming based on dynamic Bayesian network. Journal of ZheJiang University (Engineering Science), 2021, 55(7): 1339-1350.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2021.07.013     OR     https://www.zjujournals.com/eng/Y2021/V55/I7/1339


基于动态贝叶斯网络的TBM卡机风险预测

为了预测隧道机械施工时隧道掘进机（TBM）卡机风险，基于动态贝叶斯网络（BN）分析卡机概率系统. 通过专家知识以及解释结构模型确定风险因素和风险事件的因果关系，收集国内隧道机械施工地质条件实测数据，根据相关规范、研究成果和云模型云间划分法对风险指标进行区间划分，运用粗糙集分类原理对数据进行离散化，获取风险因素的原始先验概率和风险事件的条件概率. 结合软件GENIE，建立动态BN模型预测卡机风险. 结果表明：在无证据条件下，TBM卡机风险概率为8%；造成TBM卡机的关键风险因素是岩石类型、大量的地下水和断裂破碎带. TBM卡机的关键致因链为岩石类型→掌子面突泥涌沙→卡刀盘→卡机，大量的地下水→掌子面突泥涌沙→卡刀盘→卡机，高地应力→软岩大变形→卡护盾→卡机，围岩坍塌→卡护盾→卡机.


关键词： 隧道掘进机(TBM),  卡机风险预测,  贝叶斯网络(BN),  粗糙集,  云模型 
Fig.1 Brief diagram of Bayesian network
Fig.2 Flow chart of TBM jamming risk assessment model
风险指标 符号 风险指标 符号
岩石类型 A1 刀盘结构的设计 B3
大量的地下水 A2 TBM操作 C1
高地应力 A3 前期地质调查 C2
复合地层 A4 卡刀盘 M1
断裂破碎带 A5 卡护盾 M2
围岩等级 A6 姿态偏差 M3
隧道埋深 A7 围岩坍塌 M4
平曲线半径 A8 岩爆 M5
TBM选型 B1 突泥涌沙 M6
开口率的设计 B2 软岩大变形 M7
Tab.1 Risk factors of TBM construction card machine
Fig.3 Original distribution of statistical data for soft rock large deformation
云编号 Ex En He d
1 10 3.241 5 0.402 4 0.764 65
2 15 3.297 4 0.411 4 0.752 67
3 20 3.345 6 0.422 8 1.154 85
4 28 3.581 7 0.432 1 0.971 80
5 35 3.624 4 0.442 2 0.672 89
6 40 3.697 4 0.452 3 0.674 84
7 45 3.711 8 0.462 1 2.675 84
8 65 3.762 5 0.468 9
Tab.2 Numerical characteristics of initial cloud model and distance between adjacent clouds for soft rock large deformation
Fig.4 Final results of cloud model division for soft rock large deformation
符号 风险指标 分级依据 分级状态
1 2 3 4 5
M3 姿态偏差 偏移量/mm $\left[ {0,20} \right)$ $\left[ {20,50} \right)$ $\left[ {50,100} \right)$ $\left[ {100, + \infty } \right)$
M6 突泥涌沙 掌子面涌水量/（m3·h?1） $\left[ {400, + \infty } \right)$ $\left[ {200,400} \right)$ $\left[ {100,200} \right)$ $\left[ {0,100} \right)$
M7 软岩大变形 围岩收敛值/mm $\left[ {0,20} \right)$ $\left[ {20,40} \right)$ $\left[ {40,55} \right)$ $\left[ {55, + \infty } \right)$
A2 大量的地下水 单位涌水量/（L·s?1） $\left[ {100, + \infty } \right)$ $\left[ {60,100} \right)$ $\left[ {20,60} \right)$ $\left[ {0,20} \right)$
A3 高地应力 围岩强度应力比 $\left[ {0.45, + \infty } \right)$ $\left[ {0.28,0.45} \right)$ $\left[ {0.2,0.28} \right)$ $\left[ {0.14,0.2} \right)$ $\left[ {0,0.14} \right)$
A4 复合地层 横向地层复合比/% $\left[ {50,60} \right)$ $\left[ {60,70} \right)$ $\left[ {30,50} \right)$ $\left[ {0,30} \right)$ $\left[ {70, + \infty } \right)$
A5 断裂破碎带 破碎带宽度/m $\left[ {30, + \infty } \right)$ $\left[ {10,30} \right)$ $\left[ {5,10} \right)$ $\left[ {1,5} \right)$ $\left[ {0,1} \right)$
A6 围岩等级 纵波波速/（km·s?1） $\left[ {0,1.5} \right)$ $\left[ {1.5,2.5} \right)$ $\left[ {2.5,3.5} \right)$ $\left[ {3.5,4.5} \right)$ $\left[ {4.5, + \infty } \right)$
A7 隧道埋深 埋深/m $\left[ {0,\left( {2 \sim 3} \right){h_a}} \right)$ $\left[ {\left( {2 \sim 3} \right){h_a},500} \right)$ $\left[ {500, + \infty } \right)$
A8 平曲线半径 半径/mm $\left[ {0,350} \right)$ $\left[ {350,500} \right)$ $\left[ {500,600} \right)$ $\left[ {600, + \infty } \right)$
B2 开口率的设计 开口率/% $\left[ {0,k} \right)$ $k$ $\left[ {k,100} \right)$
C2 前期地质调查 勘测结果准确率/% $\left[ {0,55} \right)$ $\left[ {55,70} \right)$ $\left[ {70,80} \right)$ $\left[ {80,95} \right)$ $\left[ {95,100} \right)$
Tab.3 Grading standard for quantitative evaluation index of TBM jamming risk index
符号 风险指标 分级依据 分级状态
1 2 3 4
G 卡机 是否卡机 是 否
M1 卡刀盘 是否卡刀盘 是 否
M2 卡护盾 是否卡护盾 是 否
M4 围岩坍塌 围岩形态 稳定 部分坍塌 严重坍塌
M5 岩爆 岩爆烈级 无岩爆 弱岩爆 中等岩爆 强烈岩爆
A1 岩石类型 风险描述 强可溶岩 中等可溶岩 弱可溶岩 非可溶岩
B1 TBM选型 风险描述 不宜选择TBM施工 TBM型号选择不当 TBM型号选择适当
B3 刀盘设计 风险描述 刀盘直径不合理 刀具设计不合理 刀盘结构形式不合理 刀盘设计合理
C1 TBM操作 风险描述 地质条件复杂，
推进速度控制困难 		地质条件良好，
操作人员操作失误 		合理换步，并选择合理
的施工参数
Tab.4 Grading standard for qualitative evaluation index of TBM jamming risk
Fig.5 Correlation of risk factors and risk events for TBM jamming
Fig.6 Bayesian network topology on TBM jamming
区间组别 A1 A2 A3 A4 A5 A6 A7 A8 B1 B2 B3 C1 C2 M1 M2 M3 M4 M5 M6 M7 G
1 3 3 3 4 5 3 2 3 3 2 4 3 2 2 2 1 2 2 3 3 2
2 4 4 5 3 5 2 3 3 3 2 4 3 4 2 2 1 2 3 4 2 2
3 2 2 4 4 4 2 2 3 3 2 2 1 5 2 1 2 2 3 4 2 1
4 4 3 3 2 4 3 2 4 3 2 2 2 2 2 2 1 1 1 4 3 2
5 3 2 3 1 3 3 2 4 3 2 4 3 3 2 2 1 1 1 3 3 2
$ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $
400 3 4 3 1 5 4 3 3 3 3 2 3 2 1 2 2 1 2 2 4 1
Tab.5 TBM card machine decision table (part)
符号 风险因素指标 P
状态1 状态2 状态3 状态4 状态5
A1 岩石类型 0.114 0.354 0.287 0.245
A2 大量地下水 0.050 0.125 0.543 0.282
A3 高地应力 0.342 0.334 0.139 0.103 0.082
A4 复合地层 0.085 0.082 0.124 0.667 0.042
A5 断裂破碎带 0.085 0.124 0.221 0.221 0.349
A6 围岩等级 0.024 0.094 0.187 0.425 0.270
A7 隧道埋深 0.015 0.930 0.055
A8 平曲线半径 0 0.054 0.292 0.654
B1 TBM选型 0.045 0.024 0.931
B2 开口率的设计 0.124 0.721 0.155
B3 刀盘设计 0.097 0.134 0.024 0.745
C1 TBM操作 0.088 0.024 0.888
C2 前期地质调查 0.124 0.115 0.167 0.211 0.383
Tab.6 Prior probabilities of risk factors
A5 A8 C1 M6 $P$
状态1 状态2 状态3 状态4
1 1 1 1 0 0 0.138 0.872
1 1 1 2 0 0 0.336 0.674
1 1 1 3 0.104 0.221 0.223 0.452
1 1 1 4 0.110 0.142 0.325 0.423
1 1 2 2 0 0.271 0.274 0.455
$ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $ $ \vdots $
5 4 3 2 0 0.372 0.542 0.086
5 4 3 3 0.212 0.245 0.342 0.201
5 4 3 4 0.844 0.131 0.025 0
Tab.7 Conditional probability of risk events M3 (part)
Fig.7 Geological longitudinal section map of Shuangsan tunnel
Fig.8 Bayesian network model of TBM jamming without evidence
Fig.9 Bayesian model of TBM jamming with evidence (section TBM2)
Fig.10 Bar charts of prior probability (left) and posterior probability (right) without evidence
研究区间 条件 PJ/% F C ′ F ′ 现场是否卡机
原始数据 无证据 8 A1、A2、A5 A1→M6→M1→G、
A2→M6→M1→G、
A3→M7→M2→G、
M4→M2→G 		A1、A2、A5
21+004.542~21+543.213 A1=3、A2=2、A3=1、A4=4、A5=4、
A6=2、A7=2、A8=3、B1=3、B2=2、
B3=4、C1=1、C2=4 		0 A5、A6 A1→M6→M1→G、
A2→M6→M1→G、
A3→M7→M2→G、
M4→M2→G 		A1、A2、A5 否
40+022.111~41+025.123 A1=4、A2=2、A3=4、A4=2、A5=3、
A6=4、A7=2、A8=4、B1=3、B2=2、
B3=4、C1=3、C2=3 		53 A5、M4、M2 A1→M6→M1→G、
A2→M6→M1→G、
A3→M7→M2→G、
M4→M2→G 		A1、A2、A5 是
50+409.187~51+011.235 A1=2、A2=1、A3=4、A4=3、A5=2、
A6=2、A7=2、A8=3、B1=3、B2=2、
B3=4、C1=1、C2=4 		47 M6、M1 A1→M6→M1→G、
A2→M6→M1→G、
A3→M7→M2→G、
M4→M2→G 		A1、A2、A5 是
Tab.8 Statistical table of risk factors for TBM jamming
风险定义 P0/% P1/%
极高风险 $ \geqslant 40$ $ \geqslant 65$
高风险 $30 \sim 40$ $37 \sim 65$
中等风险 $20 \sim 30$ $18 \sim 38$
低风险 $10 \sim 20$ $2 \sim 18$
极低风险 $ \leqslant 10$ $ \leqslant 2$
Tab.9 Probability of model and field for TBM jamming
Fig.11 Scene photo of tunnel collapse
Fig.12 Scene photo of water inrush
[1]   龚秋明. 掘进机隧道掘进概论[M]. 北京: 科学出版社, 2014: 51.
[2]   SOUSA R L Risk analysis for tunneling project[J]. Tunnelling and Underground Space Technology incorporating Trenchless Technology Research, 2010, 44 (8): 103- 121
[3]   ABDELGAWAD M, FAYEK A R Fuzzy reliability analyzer: quantitative assessment of risk events in industry using fuzzy fault-tree analysis[J]. Journal of Construction Engineering and Management, 2011, 137 (4): 294- 302
doi: 10.1061/(ASCE)CO.1943-7862.0000285
[4]   SOUSA R L, EINSTEIN H H Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study[J]. Tunnelling and Underground Space Technology incorporating Trenchless Technology Research, 2011, 27 (1): 86- 100
[5]   MOLLON G, DIAS D, SOUBRA A H, et al Extension of CSRSM for the parametric study of the face stability of pressurized tunnels[J]. Geotechnical Special Publication, 2011, 43 (9): 1149- 1156
[6]   WANG Y, NI X, WANG J, et al A comprehensive investigation on the fire hazards and environmental risks in a commercial complex based on fault tree analysis and the analytic hierarchy process[J]. International Journal of Environmental Research and Public Health, 2020, 17 (19): 7347
doi: 10.3390/ijerph17197347
[7]   HYUN K C, MIN S, CHOI H, et al Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels[J]. Tunnelling and Underground Space Technology incorporating Trenchless Technology Research, 2015, 49 (6): 121- 129
[8]   周宗青, 李术才, 李利平, 等 浅埋隧道塌方地质灾害成因及风险控制[J]. 岩土力学, 2013, 34 (5): 1375- 1382
ZHOU Zong-qing, LI Zhu-cai, LI Li-ping, et al Causes of geological hazards and risk control of collapse in shallow tunnels[J]. Rock and Soil Mechanics, 2013, 34 (5): 1375- 1382
[9]   佘诗刚, 林鹏 中国岩石工程若干进展与挑战[J]. 岩石力学与工程学报, 2014, 33 (3): 433- 457
SHE Shi-gang, LIN Peng Some developments and challenging issues in rock engineering field in china[J]. Chinese Journal of Rock Mechanics and Engineering, 2014, 33 (3): 433- 457
[10]   陈发达, 吴贤国, 王彦玉, 等 基于贝叶斯网络的土压盾构刀盘失效故障诊断[J]. 土木工程与管理学报, 2017, 34 (6): 57- 62
CHEN Fa-da, WU Xian-guo, WANG Yan-yu, et al Fault diagnosis of cutterhead failure of earth pressure shield based on Bayesian network[J]. Journal of Civil Engineering and Management, 2017, 34 (6): 57- 62
doi: 10.3969/j.issn.2095-0985.2017.06.010
[11]   顾伟红, 王恩茂, 张文达 铁路隧道TBM施工风险评估[J]. 安全与环境学报, 2018, 18 (3): 843- 848
GU Wei-hong, WANG En-mao, ZHANG wen-da Risk assessment on the TBM construction of railway tunnels[J]. Journal of Safety and Environment, 2018, 18 (3): 843- 848
[12]   吕擎峰, 霍振升, 赵本海, 等 基于模糊层次和后果当量法的隧道塌方风险评估[J]. 隧道建设, 2018, 38 (增2): 31- 38
LU Qing-feng, HUO Zhen-sheng, ZHAO Ben-hai, et al Risk assessment of tunnel collapse based on fuzzy hierarchy and consequences equivalent method[J]. Tunnel Construction, 2018, 38 (增2): 31- 38
[13]   黄震, 傅鹤林, 张加兵, 等 基于模糊−证据理论的盾构隧道施工风险综合评价[J]. 武汉大学学报: 工学版, 2019, 52 (8): 694- 702
HUANG Zhen, FU He-lin, ZHANG Jia-bing, et al Comprehensive evaluation of shield tunnel construction risk based on fuzzy-evidence theory[J]. Engineering Journal of Wuhan University, 2019, 52 (8): 694- 702
[14]   REN T, JIANG D Y, CHEN J, et al Tunnel surrounding rock classification forecast based on the geological advanced prediction system in seismic area[J]. Applied Mechanics and Materials, 2012, 226-228: 910- 915
doi: 10.4028/www.scientific.net/AMM.226-228.910
[15]   王效俐, 刘潇, 苏强 邻域粗糙集融合贝叶斯神经网络在医疗决策中的应用研究[J]. 工业工程与管理, 2016, 21 (5): 141- 147
WANG Xiao-li, LIU Xiao, SU Qiang Application in medical decision making based on neighborhood rough set and Bayes neural network[J]. Industrial Engineering and Management, 2016, 21 (5): 141- 147
doi: 10.3969/j.issn.1007-5429.2016.05.022
[16]   朱敏, 刘卫东 基于粗糙集贝叶斯网络的电子产品设计缺陷评估模型[J]. 计算机应用研究, 2013, 30 (3): 706- 711
ZHU Min, LIU Wei-dong Prediction models of electronic products design defects based on rough set Bayesian network theory[J]. Application Research of Computers, 2013, 30 (3): 706- 711
doi: 10.3969/j.issn.1001-3695.2013.03.016
[17]   吕启深, 曾辉雄, 姚森敬, 等 基于贝叶斯网络和粗糙集约简的变压器故障诊断[J]. 中国电力, 2013, 46 (9): 75- 79
LU Qi-shen, ZENG Hui-xiong, YAO Sen-jing, et al Transformer fault diagnosis method based on Bayesian network and rough set reduction theory[J]. Electric Power, 2013, 46 (9): 75- 79
doi: 10.3969/j.issn.1004-9649.2013.09.017
[18]   胡记磊, 唐小微, 裘江南 基于贝叶斯网络的地震液化概率预测分析[J]. 岩土力学, 2016, 37 (6): 1745- 1752
HU Ji-lei, TANG Xiao-wei, QIU Jian-gnan Prediction of probability of seismic-induced liquefaction based on Bayesian network[J]. Rock and Soil Mechanics, 2016, 37 (6): 1745- 1752
[19]   王晨晖, 袁颖, 周爱红, 等 基于粗糙集优化支持向量机的泥石流危险度预测模型[J]. 科学技术与工程, 2019, 19 (31): 70- 77
WANG Chen-hui, YUAN Ying, ZHOU Ai-hong, et al Prediction model of debris flow danger degree based on support vector machine optimized by rough set[J]. Science Technology and Engineering, 2019, 19 (31): 70- 77
doi: 10.3969/j.issn.1671-1815.2019.31.009
[20]   王洪利 一种参照模糊集的云模型集合论方法研究[J]. 智能系统学报, 2020, 15 (3): 507- 513
WANG Hong-li A method of cloud model referring to fuzzy sets[J]. GAAI Transacting on Intelligent Systems, 2020, 15 (3): 507- 513
[21]   李兴生, 李德毅 一种基于云模型的决策表连续属性离散化方方法[J]. 模式识别与人工智能, 2003, 16 (1): 33- 38
LI Xing-sheng, LI De-yi A discretization method for continuous attributes of decision table based on cloud model[J]. Pattern Recognition and Artificial Intelligence, 2003, 16 (1): 33- 38
doi: 10.3969/j.issn.1003-6059.2003.01.007
[22]   陈馈, 张振川, 李涛. TBM设计与施工[M]. 北京: 人民交通出版社, 2018: 85-101.
[23]   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: 20-21.
[24]   廖湘平, 龚国芳, 彭雄斌, 等 基于黏性耦合机理的TBM刀盘脱困特性[J]. 浙江大学学报: 工学版, 2016, 50 (5): 902- 912
LIAO Xiang-ping, GONG Guo-fang, PENG Xiong-bin, et al Jam breakout characteristic of tunnel boring machine based on hydro−viscous drive mechanism[J]. Journal of Zhejiang University: Engineering Science, 2016, 50 (5): 902- 912
doi: 10.3785/j.issn.1008973X.2016.05.013
[25]   邓铭江, 谭忠盛 超特长隧洞TBM集群试掘进阶段适应性分析[J]. 隧道建设, 2019, 39 (1): 1- 22
DENG Ming-jiang, TAN Zhong-sheng Analysis of adaptability of TBM in trial boring stage of super-long tunnel[J]. Tunnel Construction, 2019, 39 (1): 1- 22
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[5] Jia-shuang FAN,Sui-huai YU,Jian-jie CHU,Hui WANG,Chen CHEN,Wen-zhe CUN,Tian CHEN,Jia-yan GUO. Optimal decision-making method of design scheme in cloud service mode[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(1): 143-151.
[6] Hong-wu GUO,Lei PU,Yu-xie ZHANG,Jing WU,Rui ZHAO,Zhong-fu TAN. Optimization model for integrated complementary system of wind-PV-pump storage based on rough set theory[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(4): 801-810.
[7] 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.
[8] 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.
[9] 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[J]. Journal of ZheJiang University (Engineering Science), 2019, 53(10): 1977-1985.
[10] 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.
[11] XU Xiang, HUANG Qiao, REN Yuan. Local variable weight and cloud theory applied in suspension bridge comprehensive assessment[J]. Journal of ZheJiang University (Engineering Science), 2017, 51(8): 1544-1550.
[12] 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.
[13] 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.
[14] 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-.
[15] 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.