Abstract
In this paper, a series of studies are carried out on the hidden karst encountered in the excavation of Baziling tunnel. In this paper, the safe thickness of the water-resisting layer in a hidden karst cave and tunnel is studied by means of engineering geological investigation, numerical simulation, and neural network. The numerical calculation model is established through the geological survey. Innovate to use BP neural network and differentiation algorithm to inverse the rock mechanical parameters. By analyzing the influence of different thicknesses of the water-resisting layer on the deformation and failure of surrounding rock, the final thickness of the water-resisting layer is obtained. At the same time, the influence of water pressure on the thickness of the water-resisting layer is studied, and the treatment scheme under different water pressure is finally determined.
Highlights
Karst water inrush does great harm to tunnel construction
When the thickness of the water-resisting layer is more than 10 m, the change of water pressure in the karst cave has no impact on the tunnel face, and the deformation is 0.06 m
During the construction of the tunnel through the karst area, the existing karst cave often leads to local collapse, block falling and rock falling during the tunnel excavation, especially the hidden karst cave that is not exposed during the excavation
Summary
Karst water inrush does great harm to tunnel construction. The development of the karst cave causes the instability of tunnel surrounding rock and the disaster of water and mud inrush, which is difficult to control. Li et al [2] proposed an accurate and feasible systematic evaluation method for water inrush risk of karst tunnel. Wang et al [4] combined the weighting method with the normal cloud model and proposed a new water inrush evaluation method. Based on the extension evaluation method, Zhang et al [6] proposed an improved water inrush risk evaluation system for carbonate karst tunnel. The system considers karst geological conditions and selects 9 main factors affecting tunnel water inrush as evaluation indexes. Wang et al [7] proposed a risk assessment method for water inrush and water inrush interval of karst tunnel. Zhu et al [12] proposed a fuzzy comprehensive evaluation method for water inrush risk of tunnel water rich fault based on grey theory. The reasonable safe thickness is determined, and the relevant advance grouting scheme is designed
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