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Risk assessment of water inrush in karst tunnels excavation based on normal cloud model

  • Xintong Wang
  • Shucai Li
  • Zhenhao XuEmail author
  • Jie Hu
  • Dongdong Pan
  • Yiguo Xue
Original Paper

Abstract

Water inrush in karst tunnels is a dynamic process in which internal and external factors are involved. The evaluation of this process is fuzzy, complex, and uncertain. In the current research, few articles give full consideration to the fuzziness and randomness of the water inrush evaluation with useful dynamic feedback. A new assessment method has been proposed for the water inrush evaluation based on a combination of the weighting method and normal cloud model. Specifically, an evaluation index system is forged and each index is quantitatively classified into four grades. A synthetic weighted algorithm combining the analytic hierarchy process, entropy method, and statistical methods is proposed to assign the index weight rationally. Based on the cloud generator algorithm, three numerical characteristics are calculated and a sufficient number of cloud droplets are generated. The membership degree of each index belonging to each grade is constructed and the integrated certain grades are determined. In this paper, the multi-factor normal cloud assessment method is applied to the risk assessment of the Qiyueshan tunnel. The assessment result of the risk grade is accurate, that is, the water inrush risk of different samples at the same risk grade can be reflected in figures. The results not only show high consistency with other assessment methods but are also in good agreement with the excavation results. The proposed cloud model method demonstrates good practical reference for risk assessment of tunnel construction in karst areas and can be applied to tunneling, mining, and other engineering practices in the future.

Keywords

Water inrush Karst tunnel Risk assessment Normal cloud model Combination weighting 

Notes

Acknowledgements

We would like to acknowledge the financial support from the National Natural Science Foundation of China (Grant No.s: 51509147, 51379112), the promotive research fund for excellent young and middle-aged scientists of Shandong Province (Grant No.: BS2014NJ004) and The Fundamental Research Funds of Shandong University (Grant No.s: 2017JC002, 2017JC001).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Geotechnical & Structural Engineering Research CenterShandong UniversityJinanChina

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