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) |
1 | 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. |
2 | 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. DOI |
3 | 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. DOI |
4 | 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. DOI |
5 | 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. DOI |
6 | 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. DOI |
7 | 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. DOI |
8 | 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. DOI |
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 | 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. DOI |
11 | 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. DOI |
12 | 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. DOI |
13 | 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. DOI |
14 | 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 DOI |
15 | 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. DOI |
16 | Heckerman, D. (1997), "Bayesian networks for data mining", Data Mining Knowledge Discovery, 1(1), 79-119. https://doi.org/10.1023/A:1009730122752 DOI |
17 | 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. DOI |
18 | 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. |
19 | 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. DOI |
20 | 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. DOI |
21 | 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. DOI |
22 | 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. DOI |
23 | 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. DOI |
24 | 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. DOI |
25 | 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. DOI |
26 | 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. DOI |
27 | 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. DOI |
28 | 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. DOI |
29 | 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. DOI |
30 | 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. DOI |
31 | 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. DOI |
32 | 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. DOI |
33 | Smith, N.J., Merna, T. and Jobling, P. (1999), Managing Risk in Construction Projects. Blackwell Wiley, New York, NY, U.S.A. |
34 | 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. DOI |
35 | Wang, D. (2017), "Water inrush analysis of water-bearing faults in submarine tunnels and research on water inrush risk prediction methods", Shandong University. |
36 | 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. DOI |
37 | 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. DOI |
38 | 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. DOI |
39 | 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. DOI |
![]() |