Water Inflow Amount Prediction for Karst Tunnel with Steady Seepage Conditions

Xianmeng Zhang, Minghao Wang, Dan Feng and Jingchun Wang ()
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Xianmeng Zhang: School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Minghao Wang: School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Dan Feng: School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Jingchun Wang: School of Safety Engineering and Emergency Management, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

Sustainability, 2023, vol. 15, issue 13, 1-15

Abstract: Tunnel engineering is an important aspect of developing and utilizing underground spaces. Tunnel water inrush became a common problem that restricts the safe and efficient construction of tunnels. This paper focuses on a karst water-rich tunnel in Chongqing and establishes a seepage field distribution model around the tunnel, analyzing the evolution law of the seepage field. The water balance method and underground runoff modulus method are used to predict tunnel water inflow. The prediction method for tunnel water inflow in water-rich karst areas is combined with long-term on-site tunnel hydrology observations. The distribution of groundwater in front of the tunnel face is drawn using the software, successfully predicting the larger karst area in front of the face. The prediction of water inrush risk level for karst tunnels is carried out using the SVR model. An expression formula for the water head around the tunnel is established by using the conformal mapping relationship, and the distribution status of the seepage field around the tunnel is ultimately determined. The overall prediction accuracy of the underground runoff modulus method is better than that of the water balance method in predicting the water inrush volume of the tunnel. The prediction of the large karst area ahead of the heading is successfully achieved by using the SVR model. This prediction method can provide reference and guidance for the construction of other karst-rich water tunnels in the region.

Keywords: tunnel; karst; inflow amount; steady seepage; SVR (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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