Geomechanics and Engineering

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An optimal classification method for risk assessment of water inrush in karst tunnels based on grey system theory

  • Zhou, Z.Q. (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Li, S.C. (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Li, L.P. (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Shi, S.S. (Geotechnical and Structural Engineering Research Center, Shandong University) ;
  • Xu, Z.H. (Geotechnical and Structural Engineering Research Center, Shandong University)
  • Received : 2013.09.18
  • Accepted : 2014.09.17
  • Published : 2015.05.25
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Abstract

Engineers may encounter unpredictable cavities, sinkholes and karst conduits while tunneling in karst area, and water inrush disaster frequently occurs and endanger the construction safety, resulting in huge casualties and economic loss. Therefore, an optimal classification method based on grey system theory (GST) is established and applied to accurately predict the occurrence probability of water inrush. Considering the weights of evaluation indices, an improved formula is applied to calculate the grey relational grade. Two evaluation indices systems are proposed for risk assessment of water inrush in design stage and construction stage, respectively, and the evaluation indices are quantitatively graded according to four risk grades. To verify the accuracy and feasibility of optimal classification method, comparisons of the evaluation results derived from the aforementioned method and attribute synthetic evaluation system are made. Furthermore, evaluation of engineering practice is carried through with the Xiakou Tunnel as a case study, and the evaluation result is generally in good agreement with the field-observed result. This risk assessment methodology provides a powerful tool with which engineers can systematically evaluate the risk of water inrush in karst tunnels.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation

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