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Model on Improved Variable Weight-Matter Element Theory for Risk Assessment of Water Inrush in Karst Tunnels

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Abstract

This paper presents an improved risk assessment model to evaluate the risk of water inrush in karst tunnels based on matter element theory and ideal point method. The 4 first-grade evaluation indexes and 13 s-grade factors are selected to establish the evaluation index system by considering the occurrence conditions of water inrush. All second-grade evaluation indexes are quantitatively divided into four risk grades. Based on improved Analytical Hierarchy Process and Triangular Fuzzy Number, the constant weights are obtained. The variable weights are determined according to the state variable weight theory and the values of evaluation indexes. The risk grade of water inrush is recognized by the closeness degree analysis. The proposed risk assessment model was applied to 3# inclined shaft of Yuelongmen tunnel, and the accuracy of the assessment result was verified by comparing with advance geological forecast and excavation. This proposed model provides a practical tool to evaluate the risk of water inrush in karst tunnels.

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References

  • Beaudequin D, Harden F, Roiko A, Mengersen K (2017) Potential of Bayesian networks for adaptive management in water recycling. Environ Modell Softw 91:251–270

    Article  Google Scholar 

  • Bu L, Li SC, Shi SS, Zhao Y, Zhou ZQ, Nie LC, Sun HF (2019) Application of the comprehensive forecast system for water-bearing structures in a karst tunnel: a case study. Bull Eng Geol Environ 78:357–373

    Article  Google Scholar 

  • Bukowski P (2011) Water hazard assessment in active shafts in upper silesian coal basin mines. Mine Water Environ 30(4):302–311

    Article  Google Scholar 

  • Chu HD, Xu GL, Noriyuki Y, Zhang Y, Liu P, Wang JF (2017) Risk assessment of water inrush in karst tunnels based on two-class fuzzy comprehensive evaluation method. Arab J Geosci 10:179

    Article  Google Scholar 

  • Hu JH, Jing JM, Deng YL, Gao C (2018) Comprehensive assessment on water inrush hazard of railway tunnel based on AHP-cloud model. Sci Technol Dev 14(04):311–317

    Google Scholar 

  • Huang XJ (2013) Influence factors of water bursting and mud bursting of karst tunnels and its countermeasures. J Rail Eng Soc 30(01):45–48

    Google Scholar 

  • Huang X, Lin P, Xu ZH, Li SC, Pan DD, Gao B, Li ZF (2018) Prevention structure assessment method against water and mud inrush in karst tunnels and its application. J Cent South Univ (Sci Technol) 49(10):2533–2544

    Google Scholar 

  • Khademi Hamidi J, Shahriar K, Rezai B, Rostami J, Bejari H (2010) Risk assessment based selection of rock TBM for adverse geological conditions using Fuzzy-AHP. Bull Eng Geol Environ 69(4):523–532

    Article  Google Scholar 

  • Li HX (2004) Fuzzy decision making based on variable weights. Math Comput Model 39(2–3):163–179

    Article  Google Scholar 

  • Li LP, Lei T, Li SC, Xu ZH, Xue YG, Shi SS (2015) Dynamic risk assessment of water inrush in tunnelling and software development. Geomech Eng 9(1):57–81

    Article  Google Scholar 

  • Li SC, Zhou ZQ, Li LP, Lin P, Xu ZH, Shi SS (2016) A new quantitative method for risk assessment of geological disasters in underground engineering: attribute interval evaluation theory (AIET). Tunn Undergr Space Technol 53:128–139

    Article  Google Scholar 

  • Li LP, Sun SQ, Wang J, Yang WM, Song SG, Fang ZD (2020a) Experimental study of the precursor information of the water inrush in shield tunnels due to the proximity of a water-filled cave. Int J Rock Mech Min Sci 130:104320

    Article  Google Scholar 

  • Li LP, Sun SQ, Wang J, Song SG, Fang ZD, Zhang MG (2020b) Development of compound EPB shield model test system for studying the water inrushes in karst regions. Tunn Undergr Space Technol 101:103404

    Article  Google Scholar 

  • Li SC, Wang K, Li LP, Zhou ZQ, Shi SS, Liu S (2017) Mechanical mechanism and development trend of water-inrush disasters in karst tunnels. Chin J Theor Appl Mech 49(01):22–30

    Google Scholar 

  • Li SC, Xu ZH, Huang X, Lin P, Zhao XC, Zhang QS, Yang L, Zhang X, Sun HF, Pan DD (2018) Classification, geological identification, hazard mode and typical case studies of hazard-causing structures for water and mud inrush in tunnels. Chin J Rock Mech Eng 37(05):1041–1069

    Google Scholar 

  • Li SC, Zhou ZQ, Li LP, Xu ZH, Zhang QQ, Shi SS (2013) Risk assessment of water inrush in karst tunnels based on attribute synthetic evaluation system. Tunn Undergr Space Technol 38:50–58

    Article  Google Scholar 

  • Li ZY, Wang YC, Liu Y, Jiao QL, Wang MT, Zhang Y (2019) Model on variable weight−target approaching for risk assessment of water and mud inrush in intrusive contact tunnels. J Cent South Univ (Sci Technol) 50(11):2773–2782

    Google Scholar 

  • Lin CJ, Zhang M, Zhou ZQ, Li LP, Shi SS, Chen YX, Dai WJ (2020) A new quantitative method for risk assessment of water inrush in karst tunnels based on variable weight function and improved cloud model. Tunn Undergr Space Technol 95:103136

    Article  Google Scholar 

  • Lu Z, Wu L, Zhuang X, Rabczuk, (2016) Quantitative assessment of engineering geological suitability for multilayer urban underground space. Tunn Undergr Space Technol 59:65–76

    Article  Google Scholar 

  • Morsali M, Nakhaei M, Rezaei M, Hassanpour J, Nassery H (2017) A new approach to water head estimation based on water inflow into the tunnel (case study: Karaj water conveyance tunnel). Q J Eng Geol Hydrog 50(2):126–132

    Article  Google Scholar 

  • Nezarat H, Sereshki F, Ataei M (2015) Ranking of geological risks in mechanized tunnelling by using fuzzy analytical hierarchy process (FAHP). Tunn Undergr Space Technol 31(50):358–364

    Article  Google Scholar 

  • Tu W, Li L, Shang C, Liu S, Zhu Y (2019) Comprehensive risk assessment and engineering application of mine water inrush based on normal cloud model and local variable weight. Energy Source Part A. https://doi.org/10.1080/15567036.2019.1696427

    Article  Google Scholar 

  • Wang YC, Jing HW, Yu LY, Su HJ, Luo N (2016) Set pair analysis for risk assessment of water inrush in karst tunnels. Bull Eng Geol Environ 76(3):1199–1207

    Article  Google Scholar 

  • Wang S, Wen T, Ying S, Cai F, Pang B (2017) Application of attribute model with varying weights in risk identification of high and steep slope in three gorges reservoir area and engineering application. Ecology Environ Monit Three Gorges 2(4):59–65

    Google Scholar 

  • Wang J, Li SC, Li LP, Lin P, Xu ZH, Gao CL (2019a) Attribute recognition model for risk assessment of water inrush. Bull Eng Geol Environ 78(2):1057–1071

    Article  Google Scholar 

  • Wang XT, Li SC, Xu ZH, Hu J, Pan DD, Xue YG (2019b) Risk assessment of water inrush in karst tunnels excavation based on normal cloud model. Bull Eng Geol Environ 78:3783–3798

    Article  Google Scholar 

  • Wang S, Li SC, Li LP, Shi SS, Zhou ZQ, Cheng S, Hu HJ (2019c) Study on early warning method for water inrush in tunnel based on fine risk evaluation and hierarchical advance forecast. Geosci 9(9):392

    Article  Google Scholar 

  • Wang S, Li LP, Cheng S, Hu HJ, Zhang MG, Wen T (2020) Risk assessment of water inrush in tunnels based on attribute interval recognition theory. J Cent South Univ 27(02):517–530

    Article  Google Scholar 

  • Wu Q, Li B (2016) Determination of variable weight interval and adjust weight parameters in the variable weight assessment model of water inrush from coal floor. J China Coal Soc 41(9):2143–2149

    Google Scholar 

  • Yuan YC, Li SS, Zhang QQ, Li LP, Shi SS, Zhou ZQ (2016) Risk assessment of water inrush in karst tunnels based on a modified grey evaluation model: Sample as Shangjiawan Tunnel. Geomech Eng 11:493–513

    Article  Google Scholar 

  • Zhang K, Tannantb DD, Zheng WB, Chen SG, Tan XR (2018) Prediction of karst for tunnelling using fuzzy assessment combined with geological investigations. Tunn Undergr Space Technol 80:64–77

    Article  Google Scholar 

  • Zhang K, Zheng WB, Xu C, Chen SG (2019) An improved extension system for assessing risk of water inrush in tunnels in carbonate karst terrain. KSCE J Civ Eng 23(5):2049–2064

    Article  Google Scholar 

  • Zhu JQ, Li TZ (2020) Catastrophe theory-based risk evaluation model for water and mud inrush and its application in karst tunnels. J Cent South Univ 27:1587–1598

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Science Foundation for Distinguished Young Scholars of China (Grant No.52025091),  Joint Funds of the National Natural Science Foundation of China (Grant No.U1934218), Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) (NO.2019JZZY010428; NO.2019JZZY010428), National Natural Science Foundation of China (51808359), Hebei Provincial Natural Science Foundation of China (E2019210356),  Natural Science Foundation of Chongqing (cstc2019jcyj-msxmX0813), Youth Science and Technology Innovation Project from Gansu Academy of Sciences(2019QN-04).

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Correspondence to Liping Li.

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Wang, S., Li, L., Cheng, S. et al. Model on Improved Variable Weight-Matter Element Theory for Risk Assessment of Water Inrush in Karst Tunnels. Geotech Geol Eng 39, 3533–3548 (2021). https://doi.org/10.1007/s10706-021-01709-y

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