Perspectives in Intelligentization of Tunnel Boring Machine (TBM)

YANG Huayong1 ZHOU Xinghai1 GONG Guofang1

(1.State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou, Zhejiang, China 310027)

【Abstract】The development status of artificial intelligence (AI) technologies, the development trend of intelligent engineering machines and the urgent national needs for the future intelligent TBMs are introduced briefly. It is pointed out that the intelligentization will be the hot spot of the tunnel engineering area and the focuses of future industry competition. The scientific challenges due to the complexity of the working environment, including state recognition and environment perception, correlation law between geological environment and operation parameters, intelligent planning and coordinated control of multi systems, are raised. In addition, the existing research foundation is analyzed and the inadequacy of the theory including environment and state perception, adaptive & dynamic control of construction parameters, multi system coordination control and multi-objective optimization are obtained. At last, some thinking from the aspects of design, manufacture and operation, such as excavation perception, the adaptive dynamic control of excavation parameter condition, the excavation parameter data collection and calculation, intelligent optimization and decision-making of tunneling parameters and the multi-system coordination intelligent control are proposed.

【Keywords】 full-face TBM; artificial intelligence; intelligentization; environment perception; adaptive & dynamic control; multi system coordination control;

【DOI】

【Funds】 National Key R & D Plan “Robot Intelligent Operating System for TBM Construction” (2017YFB1302600, 2017YFB1302602, 2017YFB1302604)

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    References

    [1] SU Nan. The development status and the future prospects of artificial intelligence [J]. Management & Technology of SME, 2017 (10): 107.

    [2] MENG Weidong, SI Linbo. Research on development trend of key areas of equipment manufacturing industry at home and abroad [J]. Journal of Yanshan University, 2015, 39 (6): 478.

    [3] LONG Zhiyang, GUO Xiaoxian. Development and application of full face tunnel boring machine [J]. Mine Construction Technology, 2017, 38 (5): 7.

    [4] CHEN Hao, WANG Long, LI Zizhou. An analysis for stagnation of a telescope TBM and techniques for breakthrough in a mountain tunnel through granite strata in northwest of China [J]. Journal of Henan Science and Technology, 2017 (17): 29.

    [5] SHI Lei, TAN Xiaofeng, OU Jifeng. Analysis and treatment of accidents in Guangzhou Subway Project with earth pressure balance shield [J]. China Homes, 2014 (9): 279.

    [6] GAO Panke, GUO Jinxin. Study of collapse treatment technology in TBM construction of Zhongtian Mountain Tunnel [J]. Henan Science and Technology, 2016 (17): 121.

    [7] ZHENG Gang, CUI Tao, CHENG Xuesong, et al. Introduction and analysis of an accident in a shield tunnel [J]. Chinese Journal of Geotechnical Engineering, 2017, 39 (S2): 132.

    [8] YUE Z Q, LEE C F, LAW K T, et al. Use of HKU drilling process monitor in slope stabilization [J]. Chinese Journal of Rock Mechanics and Engineering, 2002, 21 (11): 1685.

    [9] CHEN J, YUE Z Q. Ground characterization using breaking-action-based zoning analysis of rotary-percussive instrumented drilling [J]. International Journal of Rock Mechanics and Mining Sciences, 2015, 75: 33.

    [10] KAHRAMAN S. Estimating the penetration rate in diamond drilling in laboratory works using the regression and artificial neural network analysis [J]. Neural Processing Letters, 2016, 43 (2): 523.

    [11] FREGOSO E, GALLARDO L A, ABDESLEM J G. Structural joint inversion coupled with Euler deconvolution of isolated gravity and magnetic anomalies [J]. Geophysics, 2015, 80 (2): 67.

    [12] GALLARDO L A, MEJU M A. Structure-coupled multiphysics imaging in geophysical sciences [J]. Reviews of Geophysics, 2011, 49 (1): 1.

    [13] LI Shucai, NIE Lichao, LIU Bin, et al. 3D electrical resistivity inversion method using prior spatial shape constraints [J]. Applied Geophysics, 2013, 10 (4): 361.

    [14] WANG Tao, XIE Lijing, WANG Xibin, et al. PCD tool performance in high-speed milling of high volume fraction SiCp/Al composites [J]. The International Journal of Advanced Manufacturing Technology, 2015, 78 (9–12): 1445.

    [15] DU Zhiguo. Research on the cutting mechanism simulation and wear of TBM tools [D]. Shenyang: Northeastern University, 2011.

    [16] GONG Q M, YIN L J, SHE Q R. TBM tunneling in marble rock masses with high in situ stress and large groundwater inflow: a case study in China [J]. Bulletin of Engineering Geology and the Environment, 2013, 72 (2): 163.

    [17] BARTON Nick. TBM tunnelling in jointed and faulted rock [M]. Rotterdam: A. A. Balkema, 2000.

    [18] OKUBO S, FUKUI K, CHEN W. Expert system for applicability of tunnel boring machines in Japan [J]. Rock Mechanics and Rock Engineering, 2006, 36 (4): 305.

This Article

ISSN:2096-4498

CN: 41-1448/U

Vol 38, No. 12, Pages 1919-1926

December 2018

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Article Outline

Abstract

  • 0 Introduction
  • 1 Scientific challenges that intelligent TBM faces
  • 2 Existing bases for researches on intelligentization of TBM
  • 3 Intelligent conception of TBM
  • 4 Conclusions
  • References