ORIGINAL RESEARCH
Risk Assessment of Water Inrush in Karst Tunnels Based on the Efficacy Coefficient Method
Yingchao Wang1,2, Xin Yin1,2, Fan Geng3, Hongwen Jing1,2, Haijian Su1, Richeng Liu1
 
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1State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining
and Technology, Xuzhou, Jiangsu 221116, China
2School of Mechanics and Civil Engineering, China University of Mining and Technology,
Xuzhou, Jiangsu 221116, China
3School of Electrical and Power Engineering, China University of Mining & Technology,
Xuzhou, Jiangsu 221116, China
Online publish date: 2017-07-25
Publish date: 2017-07-25
Submission date: 2016-08-10
Acceptance date: 2016-10-15
 
Pol. J. Environ. Stud. 2017;26(4):1765–1775
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ABSTRACT
Water inrush is one of the typical geological hazards of tunnel construction in karst areas. It is necessary to predict water inrush more accurately for karst tunnels. Firstly, we created a model on risk evaluation of water inrush based on the efficacy coefficient method. Then karst hydrologic and engineering geological conditions were considered in detail, and several typical factors were selected as evaluation indexes, including formation lithology, unfavorable geology, groundwater level, and so on. Moreover, the weight coefficients of the selected evaluation indices were calculated using the analytic hierarchy process method. Furthermore, the total efficacy coefficient was presented to specify the risk grade of the evaluation samples. Finally, the risk grade of water inrush for karst tunnels is divided into four levels: severe (red), high (orange), elevated (yellow), and guarded (blue). Additionally, the model of risk assessment of water inrush was applied to Jigongling tunnel along the Fanba Expressway in China. The results show that the present evaluation results agree well with the construction situation, which also agree with the relative analysis results of attribute mathematical theory. The presented work with the efficacy coefficient method is relatively simple with strong operability, which has potential for predicting water inrush in karst tunnels.
eISSN:2083-5906
ISSN:1230-1485