Numerical Application of Safe Thickness Between a Tunnel and Surrounding Concealed Caves

Shan, Renliang; Zhang, Xiaonan; Lu, Mengji

doi:10.1007/s10706-017-0309-6

Cited by 12 publications

(4 citation statements)

References 17 publications

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“…This method comprises two types of attribute recognition models: one named the attribute recognition model in the design stage (ARM-D) and another named the attribute recognition model in the construction stage (ARM-C). Furthermore, commonly used numerical simulation methods, including the finite difference method (FDM) [36], finite element method (FEM) [37], particle flow code (PFC) [38], peridynamics (PD) [39], etc., have often been utilized to study the deformation mechanisms of the surrounding rock and support structures in karst tunnels, the protective layer thickness, the karst water seepage patterns, and risk assessment. Due to the simplification of tunnels, surrounding rocks, and karst caves as homogeneous, isotropic, and continuous media in modeling, the FEM method is able to better demonstrate its advantages [30].…”

Section: Introductionmentioning

confidence: 99%

Research on Collapse Risk Assessment of Karst Tunnels Based on BN Self-Learning

Sun,

Wang,

et al. 2024

Buildings

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The high risk of collapse is a key issue affecting the construction safety of karst tunnels. A risk assessment method for karst tunnel collapse based on data-driven Bayesian Network (BN) self-learning is proposed in this study. The finite element calculation is used to analyze the distribution law of the plastic zone of the tunnel and the karst cave surrounding rock under different combinations of parameters, and a four-factor three-level data case database is established. Through the self-learning of the BN database, a Bayesian Network model of karst tunnel collapse risk assessment with nodes of four types of karst cave parameters is established. The specific probability distribution state and sensitivity of the parameters of different types of karst caves under the condition of whether the tunnel and the karst cave plastic zone are connected or not are studied. The research results show that the distance and angle of the karst cave are the main influencing parameters of the tunnel collapse probability, and the diameter and number of the karst cave are the secondary influencing parameters. Among them, the distance, diameter, and number of karst caves are proportional to the probability of tunnel collapse, and the most unfavorable orientation of karst caves is 45° above the tunnel. When the tunnel passes through the karst area, it should avoid the radial intersection with the karst cave at the arch waist while staying away from the karst cave. The results of this work can provide a reference for the construction safety of karst tunnels under similar conditions.

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Section: Introductionmentioning

confidence: 99%

Research on Collapse Risk Assessment of Karst Tunnels Based on BN Self-Learning

Sun,

Wang,

et al. 2024

Buildings

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“…In the field of numerical simulation, Qin et al [16] analysed the distribution law of releasable elastic strain energy and failure zone under different widths of concealed karst cave by FLAC3D. Shan et al [17] proposed a comprehensive numerical analysis method to determinate the safe thickness, and its rationality and effectiveness have been proved by field tests. Pan et al [18] investigated the mechanism of lagging water inrush in tunnel construction due to the proximity of a karst cavern with confined water via numerical simulations.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Seepage and Displacement Field Evolutionary Characteristics in Water Inrush Disaster Process of Karst Tunnel

Lai

Guo

et al. 2021

Geofluids

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Water inrush of tunnel is one of the most common geological disasters in the karst strata in China. Aiming at the rock mass with a quasi-masonry structure in the water-resistant strata between karst cavity with high pressure water and tunnel and the shortcomings of theoretical analysis, traditional numerical simulation, and physics model test for describing and reflecting this special structure of rock mass, a Discrete Element Method considering the fluid-solid coupling effect and structural characteristics of rock mass is employed to study the disaster process of water inrush and the evolutionary characteristics of catastrophe information like seepage pressure and displacement under condition of different karst water pressure, tunnel depth, and lateral pressure coefficient. Research results show the following: (1) the seepage pressure and displacement increase with the increase of kart water pressure. The seepage pressure demonstrates a decreasing state from top to bottom in water-resistant strata, and the time of arrival to a stable value for the seepage pressure shows the time effect. (2) The larger the tunnel depth, the greater the coalescence and distribution scope of fracture and the more likely the water inrush to occur in a short time. The stability of water-resistant strata decreases on the whole with the growth of tunnel depth. (3) The increase of lateral pressure coefficient can restrain the fracture development and strengthen stability. The fracture state is significantly influenced by a lateral pressure coefficient. The results of numerical simulation are consistent with those obtained by a model test. Research and analysis based on energy are a promising train of thought for studying the disaster process of water inrush in a karst tunnel.

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“…Zhao et al [13] studied the stability of a rock pillar on a concealed karst cave in front of a tunnel and calculated the safe thickness of the rock pillar to prevent water inrush. Shan et al [14] analyzed the safe thickness between a tunnel and a nearby hidden cave and the factors influencing the rock-breaking mechanism and conducted numerical simulations and multiple regression analyses to study the safe distance between the tunnel and the karst cave.…”

Section: Introductionmentioning

confidence: 99%

Hydromechanical Simulation of Tunnel Excavation in Rock Considering a Nearby Karst Cave

Zhao

Zhang

Yao

2021

Mathematical Problems in Engineering

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Tunnel excavation tends to be affected by karst cavities in karst areas. Some cavities that are at low risk of causing safety issues without treatment tend to be ignored in the design and construction of tunnels to reduce costs. It is necessary to gain a better understanding of the effect of such a cavity on the seepage around a tunnel, the deformation of the surrounding rock, and the stress of the tunnel lining. In this paper, a two-dimensional rock-tunnel hydromechanical model with a karst cave was established with FLAC3D finite difference software to simulate the tunnel excavation with the consideration of seepage. Numerical simulations were performed to analyze the deformation of the surrounding rock, the seepage field of the surrounding rock, and the stress of the tunnel lining, and the results were compared for scenarios when the karst cave is at different locations relative to the tunnel. These results can provide a reference for the design and construction of tunnel engineering in rock with karst caves.

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Numerical Application of Safe Thickness Between a Tunnel and Surrounding Concealed Caves

Cited by 12 publications

References 17 publications

Research on Collapse Risk Assessment of Karst Tunnels Based on BN Self-Learning

Research on Collapse Risk Assessment of Karst Tunnels Based on BN Self-Learning

Analysis of Seepage and Displacement Field Evolutionary Characteristics in Water Inrush Disaster Process of Karst Tunnel

Hydromechanical Simulation of Tunnel Excavation in Rock Considering a Nearby Karst Cave

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