Geomechanics and Engineering

  • 제27권6호
  • /
  • Pages.605-614
  • /
  • 2021
  • /
  • 2005-307X(pISSN)
  • /
  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Using Bayesian network and Intuitionistic fuzzy Analytic Hierarchy Process to assess the risk of water inrush from fault in subsea tunnel

  • Song, Qian (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Xue, Yiguo (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Li, Guangkun (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Su, Maoxin (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Qiu, Daohong (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Kong, Fanmeng (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Zhou, Binghua (Geotechnical and Structural Engineering Research Center, Shandong University)
  • 투고 : 2021.01.19
  • 심사 : 2021.11.22
  • 발행 : 2021.12.25
⟨ 이전 논문 다음 논문 ⟩

초록

Water inrush from fault is one of the most severe hazards during tunnel excavation. However, the traditional evaluation methods are deficient in both quantitative evaluation and uncertainty handling. In this paper, a comprehensive methodology method combined intuitionistic fuzzy AHP with a Bayesian network for the risk assessment of water inrush from fault in the subsea tunnel was proposed. Through the intuitionistic fuzzy analytic hierarchy process to replace the traditional expert scoring method to determine the prior probability of the node in the Bayesian network. After the field data is normalized, it is classified according to the data range. Then, using obtained results into the Bayesian network, conduct a risk assessment with field data which have processed of water inrush disaster on the tunnel. Simultaneously, a sensitivity analysis technique was utilized to investigate each factor's contribution rate to determine the most critical factor affecting tunnel water inrush risk. Taking Qingdao Kiaochow Bay Tunnel as an example, by predictive analysis of fifteen fault zones, thirteen of them are consistent with the actual situation which shows that the IFAHP-Bayesian Network method is feasible and applicable. Through sensitivity analysis, it is shown that the Fissure development and Apparent resistivity are more critical comparing than other factor especially the Permeability coefficient and Fault dip. The method can provide planners and engineers with adequate decision-making support, which is vital to prevent and control tunnel water inrush.

키워드

과제정보

The authors would like to thank the financial support from the National Natural Science Foundation of China (grant numbers 41877239, 51379112, 51422904, 40902084 and 41772298), and Fundamental Research Fund of Shandong University (grant number 2018JC044), and Natural Science Foundation of Shandong Province (grant numbers JQ201513 and 2019GSF111028).

참고문헌

  1. Beard, A. N. (2010), "Tunnel safety, risk assessment and decision-making", Tunn. Undergr. Sp. Tech., 25(1), 91-94. https://doi.org/10.1016/j.tust.2009.07.006.
  2. Bidyuk, P.I., Terent'Ev, A.N. and Gasanov, A.S. (2005), "Construction and methods of learning of Bayesian networks", Cybernetic. Syst. Anal., 41(4), 587-598. https://doi.org/10.1007/s10559-005-0094-8.
  3. Cai, C., Huang, T., Li, X. and Li, Y.Z. (2011), "Water-inflow forecast of submarine tunnel based on BP neural networks", Appl. Mech. Mater., 90-93, 2173-2177. https://doi.org/10.4028/www.scientific.net/AMM.90-93.2173.
  4. Ding, H.H., Wu, Q., Zhao,D.K., Mu W.P. and Yu, S. (2019), "Risk assessment of karst collapse using an integrated fuzzy analytic hierarchy process and grey relational analysis model", Geomech. Eng., 18(5), 515-525. http://doi.org/10.12989/gae.2019.18.5.515
  5. Ebrahimi, M., Hedayat, A.A. and Fakhrabadi, H. (2018), "Selecting optimized concrete structure by analytic hierarchy process (ahp)", Comput. Concrete, 22(3), 327-336. http://doi.org/10.12989/cac.2018.22.3.327.
  6. Fattahi, H. and Ilghani, N.Z. (2019), "Applying Bayesian models to forecast rock mass modulus", Geotech. Geologic. Eng., 37(5), 4337-4349. http://dx.doi.org/10.1007/s10706-019-00911-3.
  7. Friedman, N., Linial, M., Nachman, I. and Pe'Er, D. (2000), "Using bayesian networks to analyze expression data", J. Comput. Biology, 7(3-4), 601-620. https://doi.org/10.1089/106652700750050961.
  8. Heckerman, D. (1997), "Bayesian networks for data mining", Data Mining Knowledge Discovery, 1(1), 79-119. https://doi.org/10.1023/A:1009730122752
  9. Jiang, Z., Ding, B, Zhang, L., Wu, X.G. and Chen,Y.Q. (2014), "Risk assessment of the operational tunnel leakage based on bayesian network", Environ. Eng., 32, 922-927.
  10. Kouchami-Sardoo, I., Shirani, H., Esfandiarpour-Boroujeni, I. and Bashari, H. (2019), "Application of a Bayesian belief network model for assessing the risk of wind erosion: A test with data from wind tunnel experiments", Aeolian Res., 41, 100543. https://doi.org/10.1016/j.aeolia.2019.100543.
  11. Hasanpour, R., Rostami, J., Schmitt, J., Ozcelik, Y. and Sohrabian, B. (2020), "Prediction of tbm jamming risk in squeezing grounds using bayesian and artificial neural networks", J. Rock Mech. Geotech. Eng., 12(1), 25-35, https://doi.org/10.1016/j.jrmge.2019.04.006.
  12. Li, L., Tu, W., Shi, S., Chen, J. and Zhang, Y. (2016), "Mechanism of water inrush in tunnel construction in karst area", Geomatic. Nat. Hazards Risk, 7(1), 35-46. https://doi.org/10.1080/19475705.2016.1181342.
  13. 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. Sp. Tech., 40, 182-191. https://doi.org/10.1016/j.tust.2013.10.005.
  14. Li, L.P., Lei, T., Li, S.C., Zhang, Q.Q., Xu, Z.H., Shi, S.S. and Zhou, Z.Q. (2015), "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.
  15. Mahmoodzadeh, A., Mohammadi, M., Noori, K.M.G., Khishe, M., Ibrahim, H.H., Ali, H.F.H. and Abdulhamid, S.N. (2021), "Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques", Automat. Constr., 127, 103719. https://doi.org/10.1016/j.autcon.2021.103719.
  16. Matsuo, S. (1986), "An overview of the Seikan tunnel project", Tunn. Undergr. Sp. Tech., 1(3-4), 323-331. https://doi.org/10.1016/0886-7798(86)90015-5.
  17. Nilsen, B. (2014), "Characteristics of water ingress in Norwegian Subsea Tunnels", Rock Mech. Rock Eng., 47(3), 933-945. https://doi.org/10.1007/s00603-012-0300-8.
  18. Nikkhah, M., Ghasvareh, M.A. and Bahalgardi, N.F. (2019), "Risk management in urban tunnels using methods of game theory and multi-criteria decision-making", J. Min. Environ., 10(3), 597-611. https://doi.org/10.22044/jme.2019.7136.1559.
  19. Peng, Y.X., Wu, L., Zuo, Q.J., Chen, C.H. and Hao, Y. (2020), "Risk assessment of water inrush in tunnel through water-rich fault based on AHP-Cloud model", Geomatic. Nat. Hazards Risk, 11(1), 301-317, https://doi.org/10.1080/19475705.2020.1722760.
  20. Saaty, T.L. (2008), "Decision making with the analytic hierarchy process", Int. J. Services Sci., 1(1), 83-98. https://doi.org/10.1504/IJSSCI.2008.017590.
  21. Sadiq, R. and Tesfamariam, S. (2009), "Environmental decision-making under uncertainty using intuitionistic fuzzy analytic hierarchy process (IF-AHP)", Stochastic Environ. Res. Risk Assessment, 23(1), 75-91. https://doi.org/10.1007/s00477-007-0197-z.
  22. Shen, R.X., Wu, X.Y., Liu, C.W. and Zeng, D.J. (2008), "Research of water inrush on subsea tunnel construction", J. Wuhan Univ. Technol. (Transport. Sci. Eng.), 385-388.
  23. Shekari, M.R. (2021), "A coupled numerical approach to simulate the effect of earthquake frequency content on seismic behavior of submarine tunnel", Mar. Struct., 75, 102848. https://doi.org/10.1016/j.marstruc.2020.102848.
  24. Shi, L., Wang, J.H., Zhang, G.M, Cheng, X.D. and Zhao, X.B. (2017), "A risk assessment method to quantitatively investigate the methane explosion in underground coal mine", Process Safety Environ.. Protect., 107, 317-333. https://doi.org/10.1016/j.psep.2017.02.023.
  25. Smith, N.J., Merna, T. and Jobling, P. (1999), Managing Risk in Construction Projects. Blackwell Wiley, New York, NY, U.S.A.
  26. Sousa, R.L. and Einstein, H.H. (2011), "Risk analysis during tunnel construction using bayesian networks: porto metro case study", Tunn. Undergr. Sp. Technol., 27(1), 86-100. https://doi.org/10.1016/j.tust.2011.07.003.
  27. Su, Y.B. (2020), "Selection and application of building material suppliers based on intuitionistic fuzzy analytic hierarchy process (ifahp) model", IEEE Access, 8, 136966-136977. https://doi.org/10.1109/ACCESS.2020.3011946.
  28. Wang, C.L., Li, C.F., Chen, Z., Liao, Z.F., Zhao,G.M., Shi, F. and Yu, W.J. (2020), "Experimental investigation on multi-parameter classification predicting degradation model for rock failure using bayesian method", Geomech. Eng., 20(2), 113-120. https://doi.org/10.12989/gae.2020.20.2.113.
  29. Wang, D. (2017), "Water inrush analysis of water-bearing faults in submarine tunnels and research on water inrush risk prediction methods", Shandong University.
  30. Wu, Q. and Zhou, W.F. (2008), "Prediction of groundwater inrush into coal mines from aquifers underlying the coal seams in China: vulnerability index method and its construction", Environ. Geology, 56(2), 245-254. https://doi.org/10.1007/s00254-007-1160-5.
  31. Xu, W.F. (2009), "Study on mechanism and countermeasure of water inrush and burst mud in subsea tunnel", J. Ocean Technol., 28(2), 73-76.
  32. Xu, Z.S. and Liao, H.C. (2013), "Intuitionistic fuzzy analytic hierarchy process", IEEE Transact. Fuzzy Syst., 22(4), 749-761. https://doi.org/10.1109/TFUZZ.2013.2272585.
  33. Xue, Y.G., Li, X., Qiu, D.H., Ma, X.M., Kong, F.M., Qu, C. and Zhao, Y. (2019), "Stability evaluation for the excavation face of shield tunnel across the Yangtze River by multi-factor analysis", Geomech. Eng., 19(3), 283-293. https://doi.org/10.12989/gae.2019.19.3.283.
  34. Xue, Y.G., Kong, F.M., Li, S.C., Qiu, D.H., Su, M.X., Li, Z.Q. and Zhou, B.H. (2021a), "Water and mud inrush hazard in underground engineering: Genesis, evolution and prevention", Tunn. Undergr. Sp. Technol., 114, 103987, https://doi.org/10.1016/j.tust.2021.103987.
  35. Xue, Y.G., Kong, F.M., Li, S.C., Zhang, Q.S., Qiu, D.H., Su, M.X. and Li, Z.Q. (2021b), "China starts the world's hardest "SkyHigh Road" project: Challenges and countermeasures for Sichuan-Tibet railway", Innovation, 2(2). https://doi.org/10.1016/j.xinn.2021.100105.
  36. Xue, Y.G., Kong, F.M., Qiu, D.H., Su, M.X., Zhao, Y. and Zhang, K. (2021c), "The classifications of water and mud/rock inrush hazard: a review and update", Bull. Eng. Geology Environ., 80(3), 1907-1925, https://doi.org/10.1007/s10064-020-02012-5.
  37. Xue, Y.G., Zhou, B.H., Ge, S.Q., Qiu, D.H. and Gong, H.M. (2020), "Optimum design calculation method for the reasonable buried depth: A case study from Hong Kong-Zhuhai-Macao immersed tunnel", Ocean Eng., 206, 107275, https://doi.org/10.1016/j.oceaneng.2020.107275.
  38. Zhang, L.M., Wu, X.G., Qin, Y.W., Skibniewski, M.J. and Liu, W.L. (2016), "Towards a fuzzy bayesian network based approach for safety risk analysis of tunnel-induced pipeline damage", Risk Anal., 36(2), 278. https://doi.org/10.1111/risa.12448.
  39. Zhou, B.H., Xue, Y.G., Li, S., Qiu, D., Tao, Y., Zhang, K. and Xia, T. (2020), "Probabilistic analysis of tunnel collapse: Bayesian method for detecting change points", Geomech. Eng., 22(4), 291-303. https://doi.org/10.12989/gae.2020.22.4.291.