Journal of Mountain Science

, Volume 15, Issue 7, pp 1585–1596 | Cite as

Detection and treatment of water inflow in karst tunnel: A case study in Daba tunnel

  • Xiang-hui Li
  • Qing-song Zhang
  • Xiao ZhangEmail author
  • Xiong-dong Lan
  • Chong-hao Duan
  • Jian-guo Liu


In a karst tunnel, fissures or cracks that are filled with weathered materials are a type of potential water outlet as they are easily triggered and converted into groundwater outlets under the influence of high groundwater pressure. A terrible water inrush caused by potential water outlets can seriously hinder the project construction. Potential water outlets and water sources that surrounding the tunnel must be detected before water inflow can be treated. This paper provides a successful case of the detection and treatment of water inflow in a karst tunnel and proposes a potential water outlet detection (PWOD) method in which heavy rainfall (>50 mm/d) is considered a trigger for a potential water outlet. The Daba tunnel located in Hunan province, China, has been constructed in a karst stratum where the rock mass has been weathered intensely by the influence of two faults. Heavy rain triggered some potential water outlets, causing a serious water inrush. The PWOD method was applied in this project for the treatment of water inflow, and six potential water outlets in total were identified through three heavy rains. Meanwhile, a geophysical prospecting technique was also used to detect water sources. The connections between water outlets and water sources were identified with a 3-D graphic that included all of them. According to the distribution of water outlets and water sources, the detection area was divided into three sections and separately treated by curtain grouting.


Karst tunnel Water inrush Potential water outlet detection Geophysical prospecting technique Water inflow Grouting 


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This work was supported by the National Key Research and Development Project (Grant No. 2016YFC0801604) and Natural Science Foundation of Shandong Province (Grant No. ZR2017MEE070).


  1. Alimoradi A, Moradzadeh A, Naderi R, et al. (2008) Prediction of geological hazardous zones in front of a tunnel face using TSP-203 and artificial neural networks. Tunnelling and Underground Space Technology 23(6): 711–717. CrossRefGoogle Scholar
  2. Asadollahi P, Foroozan R. (2006) Comparison of the evaluated rock mass properties from the TSP system and the RMR classification (Semnan tunnel, Iran). Tunnelling and Underground Space Technology 21(3-4): 236. CrossRefGoogle Scholar
  3. Bakalowicz M. (2005) Karst groundwater: a challenge for new resources. Hydrogeology Journal 13: 148–160. CrossRefGoogle Scholar
  4. Butrón C, Gustafson G, Fransson Å, et al. (2010) Drip sealing of tunnels in hard rock: A new concept for the design and evaluation of permeation grouting. Tunnelling and Underground Space Technology 25(2): 114–121. CrossRefGoogle Scholar
  5. Day MJ. (2004) Karstic problems in the construction of Milwaukee's Deep Tunnels. Environmental Geology 24: 859–863. CrossRefGoogle Scholar
  6. Gómez-Ortiz D, Montesinos FG, Martín-Crespo T, et al. (2014) Combination of geophysical prospecting techniques into areas of high protection value: Identification of shallow volcanic structures. Journal of Applied Geophysics 109: 15–26. CrossRefGoogle Scholar
  7. Harry M, Barry B. (2018) Karst terrane and transportation issues. Encyclopedia of sustainability science and technology 2018(1): 5645–5671. Google Scholar
  8. Knez M, Tadej S, Stanka Š, et al. (2008) The largest cave discovered in a tunnel during motorway construction in Slovenia classical karst (kras). Environmental Geology 54(4): 711–718. CrossRefGoogle Scholar
  9. Kuras O, Pritchard JD, Philip I M, et al. (2009) Monitoring hydraulic processes with automated timelapse electrical resistivity tomography. Comptes Rendus Géoscience 341(7): 868–885. CrossRefGoogle Scholar
  10. Li S, Liu B, Xu X, et al. (2017) An overview of ahead geological prospecting in tunneling. Tunnelling and Underground Space Technology 63: 69–94. CrossRefGoogle Scholar
  11. Li S, Liu R, Zhang Q, et al. (2013) Research on C-S slurry diffusion mechanism with time-dependent behavior of viscosity. Chinese Journal of Rock Mechanics and Engineering 32(12): 2415–2421. (In Chinese)Google Scholar
  12. Li X, Qi Z, Xue G, et al. (2010) Three dimensional curved surface continuation image based on tem pseudo wave-field. Chinese Journal of Geophysics 53(12): 3005–3011. (In Chinese) Google Scholar
  13. Lisa H, Gunnar G, åsa F, et al. (2013) A statistical grouting decision method based on water pressure tests for the tunnel construction stage-A case study. Tunnelling and Underground Space Technology 33: 54–62. CrossRefGoogle Scholar
  14. Liu R, Li S, Zhang Q, et al. (2011) Experiment and application research on a new type of dynamic water grouting material. Chinese Journal of Rock Mechanics and Engineering 30(7): 1454–1459. (In Chinese)Google Scholar
  15. Mahmud K, Mariethoz G, Baker A, et al. (2018) Hydrological characterization of cave drip waters in a porous limestone: Golgotha Cave, Western Australia. Hydrology and Earth System Sciences 22(2): 977–988. CrossRefGoogle Scholar
  16. Mao B, Zhang G, Tang B, et al. (2016) Analysis of Mechanism of Water-inrush of the 1# Transverse of Gangwu Tunnel in the Shanghai—Kunming Passenger Special Line. Journal of railway Engineering Society (06): 83–87. (In Chinese)Google Scholar
  17. Mao D, Lu M, Zhao Z, et al. (2016) Effects of Water Related Factors on Pre-grouting in Hard Rock Tunnelling. Procedia Engineering 165: 300–307. CrossRefGoogle Scholar
  18. Mohamad FTB, Samsudin T, Roslan H, et al. (2011) Time-lapse ERT monitoring of an injection/withdrawal experiment in a shallow unconfined aquifer. Environmental Monitoring and Assessment 180(9): 345–369. Google Scholar
  19. Qiang Q, Rong X. (2008) State, issues and relevant recommendations for security risk management of China's underground engineering. Chinese Journal of Rock Mechanics and Engineering 27(4): 649–655. (In Chinese) Google Scholar
  20. Shi S, Li S, Li L, et al. (2014) Advance optimized classification and application of surrounding rock based on fuzzy analytic hierarchy process and Tunnel Seismic Prediction. Automation in Construction 37: 217–222. CrossRefGoogle Scholar
  21. Tsuji M, Kobayashi S, Mikake S, et al. (2017) Post-Grouting Experiences for Reducing Groundwater Inflow at 500 m Depth of the Mizunami Underground Research Laboratory, Japan. Procedia Engineering 191: 543–550. CrossRefGoogle Scholar
  22. Walid A. (2011) Contribution of the geophysical methods in characterizing the water leakage in Afamia B dam, Syria. Journal of Applied Geophysics 75(3): 464–471. CrossRefGoogle Scholar
  23. Xiao P, Wu X, Shi Z, et al. (2018) Helicopter TEM parameters analysis and system optimization based on time constant. Journal of Applied Geophysics 150: 84–92. CrossRefGoogle Scholar
  24. Xiao X, Xu M, Ding Q, et al. (2018) Experimental study investigating deformation behavior in land overlying a karst cave caused by groundwater level changes. Environmental Earth Sciences 77: 64. CrossRefGoogle Scholar
  25. Xu J, Huang S. (1996) Mechanism of burst mud and spring water of the Dayaoshan tunnel. Journal of Railway Engineering Society (02): 83–89. (In Chinese)Google Scholar
  26. Zhang Q, Zhang L, Zhang X, et al. (2015) Grouting diffusion in a horizontal crack considering temporal and spatial variation of viscosity. Chinese Journal of Rock Mechanics and Engineering 34(6): 1198–1210. (In Chinese) Google Scholar

Copyright information

© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Geotechnical & Structural Engineering Research CenterShandong UniversityJinanChina

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