Geomechanics and Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Risk assessment of water inrush in karst tunnels based on a modified grey evaluation model: Sample as Shangjiawan Tunnel

  • Yuan, Yong-cai (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Li, Shu-cai (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Zhang, Qian-qing (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Li, Li-ping (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Shi, Shao-shuai (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Zhou, Zong-qing (Geotechnical and Structural Engineering Research Center, Shandong University)
  • Received : 2015.12.13
  • Accepted : 2016.05.17
  • Published : 2016.10.25
⟨ Previous Next ⟩

Abstract

A modified grey clustering method is presented to systematically evaluate the risk of water inrush in karst tunnels. Based on the center triangle whitenization weight function and upper and lower limit measure whitenization weight function, the modified grey evaluation model doesn't have the crossing properties of grey cluster and meets the standard well. By adsorbing and integrating the previous research results, seven influence factors are selected as evaluation indexes. A couple of evaluation indexes are modified and quantitatively graded according to four risk grades through expert evaluation method. The weights of evaluation indexes are rationally distributed by the comprehensive assignment method. It is integrated by the subjective factors and the objective factors. Subjective weight is given based on analytical hierarchy process, and objective weight obtained from simple dependent function. The modified grey evaluation model is validated by Jigongling Tunnel. Finally, the water inrush risk of Shangjiawan Tunnel is evaluated by using the established model, and the evaluation result obtained from the proposed method is agrees well with practical situation. This risk assessment methodology provides a powerful tool with which planners and engineers can systematically assess the risk of water inrush in karst tunnels.

Keywords

Acknowledgement

Supported by : National Natural Science of China

References

  1. Beard, A.N. (2010), "Tunnel safety, risk assessment and decision-making", Tunn. Undergr. Space Technol., 25(1), 91-94. https://doi.org/10.1016/j.tust.2009.07.006
  2. Cai, X.F., Zhu, Y. and He, X.G. (2004), "A gray cluster evaluation analysis of tunneling construction technology for Beijing subway", China Civil Eng. J., 37(12), 91-96.
  3. Deng, J.L. (1982), "The grey control system", J. Huazhong Univ. Sci. Technol. (Natural Science Edition), 10(3), 9-18.
  4. Ding, L.H. (2011), "Research on estimation of slope stability based on improved grey correlation analysis and analytic hierarchy process", Rock Soil Mech., 32(11), 3437-3441.
  5. Duan, J.L., Zhang, Q.S. and Liu, W.J. (2006), "The model of information system's risk assessment based on analytic hierarchy process and grey theory", J. Guangdong Univ. Technol., 23(4), 12-16.
  6. Han, X.R. (2004), "Karst water bursting in tunnel and expert judging system", Carsologica Sinica, 23(3), 213-218.
  7. Huang, X.Z. and She, C.X. (2006), "Research on methods of surrounding rock masses stability classification based on extension theory", Rock Soil Mech., 27(10), 1800-1804.
  8. Kuang, X., Bai, M.Z., Wang, C.L. and Guo, J.X. (2010), "Research of comprehensive warning of Water inrush hazards in karst tunnel based on fuzzy evolution method", J. Highway Transport. Res. Develop., 27(11), 100-103.
  9. Li, X.P. and Li, Y.N. (2014), "Research on risk assessment system for water inrush in the karst tunnel construction based on GIS: Case study on the diversion tunnel groups of the Jinping II Hydropower Station", Tunn. Undergr. Space Technol., 40(2), 182-191. https://doi.org/10.1016/j.tust.2013.10.005
  10. Li, Y.H. and Chen, Y. (2013), "Synthetic ranking evaluation of flood disaster based on grey-cloud whitening-weight function", J. Natural Disasters, 22(1), 108-114.
  11. Li, L.P., Li, S.C., Chen, J., Li, J.L., Xu, Z.H. and Shi, S.S. (2011a), "Construction license mechanism and its application based on karst water inrush risk evaluation", Chinese J. Rock Mech. Eng., 30(7), 1345-1354.
  12. Li, X.P., Li, Y.N. and Zhou, S.P. (2011b), "Study and application of forecasting system for water inrush under high pressure in Xiamen Submarine Tunnel Construction based on GIS", Proceedings of the 3rd International Conference on Environmental Science and Information Application Technology (ESIAT 2011), 10, 999-1005.
  13. Li, S.C., Zhou, Z.Q., Li, L.P., Xu, Z.H., Zhang, Q.Q. and Shi, S.S. (2013a), "Risk assessment of water inrush in karst tunnels based on attribute synthetic evaluation system", Tunn. Undergr. Space Technol., 38(9), 50-58. https://doi.org/10.1016/j.tust.2013.05.001
  14. Li, S.C., Zhou, Z.Q., Li, L.P., Shi, S.S. and Xu, Z.H. (2013b), "Risk evaluation theory and method of water inrush in karst tunnels and its applications", Chinese J. Rock Mech. Eng., 32(9), 1858-1867.
  15. Li, L.P., Lei, T., Li, S.C., Zhang, Q.Q., Xu, Z.H., Shi, S.S. and Zhou, Z.Q. (2015a), "Risk assessment of water inrush in karst tunnels and software development", Arab. J. Geosci., 8(4), 1843-1854. https://doi.org/10.1007/s12517-014-1365-3
  16. Li, L.P., Lei, T., Li, S.C., Xu, Z.H., Xue, Y.G. and Shi, S.S. (2015b), "Dynamic risk assessment of water inrush in tunnelling and software development", Geomech. Eng., Int. J., 9(1), 57-81. https://doi.org/10.12989/gae.2015.9.1.057
  17. Liu, S.F., Dang, Y.G., Fang, Z.G. and Xie, N.M. (2010), Grey System Theory and its Application, (5th Ed.), Science Press, Beijing, China, pp. 1-8.
  18. Liu, H.Q., Shi, X. and Wang, H. (2014), "Study on dynamic qualification of university poor students based on Triangular Whitenization Weight Function", Math. Practice Theory, 44(1), 25-30.
  19. Ma, Y., Wang, X.L. and Liu, Z. (2011), "Study of the application of the multi-model fuzzy evaluation method to the risks of water-bursting in tunnels in karst regions", Traffic Eng. Technol. Nat. Defence, 5, 38-42.
  20. Mao, B.Y., Xu, M. and Jiang, L.W. (2010), "Preliminary study on risk assessment of water and mud inrush in karst tunnel", Carsologica Sinica, 29(2), 183-189.
  21. Saaty, T.L. (1990), "How to make a decision: The analytic hierarchy process", Eur. J. Operat. Res., 48(1), 9-26. https://doi.org/10.1016/0377-2217(90)90057-I
  22. Schubert, M., Hoj, N.P., Ragnoy, A. and Buvik, H. (2012), "Risk assessment of road tunnels using Bayesian Networks", Procedia - Social Behavioral Science, 48, 2697-2706. https://doi.org/10.1016/j.sbspro.2012.06.1239
  23. She, S.G. and Lin, P. (2014), "Some developments and challenging issues in rock engineering field in China", Chinese J. Rock Mech. Eng., 33(3), 433-457.
  24. Sousa, R.L. and Einstein, H.H. (2012), "Risk analysis during tunnel construction using Bayesian Networks: Porto Metro case study", Tunn. Undergr. Space Technol., 27(1), 86-100. https://doi.org/10.1016/j.tust.2011.07.003
  25. Tongji University (2003), Risk Analysis of Chongming Cross-River Project; Tongji University, Shanghai, China.
  26. Wang, M., Xu, Z.Y. and Wang, L.J. (2004), "Gushing water from Yuanliangshan Tunnel in Maoba Syncline and its impact on surface water", China Safety Sci. J., 14(5), 6-10.
  27. Xie, X.B. and Pan, C.L. (2007), "Rockburst prediction method based on Grey Whitenization weight function cluster theory", J. Hunan Univ. (Natural Sciences), 34(8), 16-20.
  28. Xu, Z.H., Li, S.C., Li, L.P., Chen, J. and Shi, S.S. (2011a), "Construction permit mechanism of karst tunnels based on dynamic assessment and management of risk", Chinese J. Geotech. Eng., 33(11), 1715-1725.
  29. Xu, Z.H., Li S.C. and Li, L.P. (2011b), "Risk assessment of water or mud inrush of karst tunnels based on analytic hierarchy process", Rock Soil Mech., 32(6), 1 757-1 765.
  30. Xue, J.G., Zhou, J. and Shi, X.Z. (2010), "Assessment of classification for rock mass blastability based on entropy coefficient of attribute recognition model", J. Central South Univ. (Science and Technology), 41(1), 251-256.
  31. Zhang, M.Q. and Liu, Z.W. (2005), "The analysis on the features of karst water burst in the Yuanliangshan tunnel", Chinese J. Geotech. Eng., 27(4), 422-426.
  32. Zhao, Y., Li, P.F. and Tian, S.M. (2013), "Prevention and treatment technologies of railway tunnel water inrush and mud gushing in China", J. Rock Mech. Geotech. Eng., 6(5), 468-477.
  33. Zhou, Z.Q., Li, S.C., Li, L.P., Shi, S.S., Song, S.G. and Wang, K. (2013), "Attribute recognition model of fatalness assessment of water inrush in karst tunnels and its application", Rock Soil Mech., 34(3), 818-826.

Cited by

  1. A New Advance Classification Method for Surrounding Rock in Tunnels Based on the Set-Pair Analysis and Tunnel Seismic Prediction System vol.36, pp.4, 2018, https://doi.org/10.1007/s10706-018-0471-5
  2. Time-varying characteristics on migration and loss of fine particles in fractured mudstone under water flow scour vol.12, pp.5, 2019, https://doi.org/10.1007/s12517-019-4286-3
  3. Combination of engineering geological data and numerical modeling results to classify the tunnel route based on the groundwater seepage vol.13, pp.4, 2017, https://doi.org/10.12989/gae.2017.13.4.671
  4. Investigation of possible causes of sinkhole incident at the Zonguldak Coal Basin, Turkey vol.16, pp.2, 2016, https://doi.org/10.12989/gae.2018.16.2.177
  5. S-I model of horizontal jet grouting reinforcement for soft soil vol.15, pp.5, 2016, https://doi.org/10.12989/gae.2018.15.5.1029
  6. Study on Early Warning Method for Water Inrush in Tunnel Based on Fine Risk Evaluation and Hierarchical Advance Forecast vol.9, pp.9, 2019, https://doi.org/10.3390/geosciences9090392
  7. Risk assessment of water inrush in tunnels based on attribute interval recognition theory vol.27, pp.2, 2016, https://doi.org/10.1007/s11771-020-4313-2
  8. Development and application of a floor failure depth prediction system based on the WEKA platform vol.23, pp.1, 2016, https://doi.org/10.12989/gae.2020.23.1.051