Mechanical Response Law and Parameter Influence Analysis of Karst Tunnel Dynamic Excavation

Han, Gang; Xue, Penghui; Wang, Yanyan; Li, Xian; Bian, Hanbing; Wang, Yixian; Guo, Panpan

doi:10.3390/app13169351

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…Su et al [12] proposed a method for the construction of the displacement release coefficient formula based on the quadratic regression orthogonal combination test design method. Han et al [13] explored the effects of various parameters on the structural stability of shield tunnels in karst areas using the orthogonal experimental design method. Li et al [14] proposed a water inrush evaluation model by applying the Mohr-Coulomb intensity criterion and carried out a sensitivity analysis of 12 specific parameters in the assessment model.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Influencing Factors of Karst Tunnel Construction Based on Orthogonal Tests and a Random Forest Algorithm

Wu,

Sun,

Meng

2024

Applied Sciences

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To conduct a sensitivity analysis of the relevant parameters that impact the mechanics of tunnel construction in karst areas, firstly, the orthogonal design and range analysis method is applied to sort the 11 kinds of karst-tunnel-influencing factors from high to low according to the sensitivity degree. Secondly, the random forest algorithm based on an orthogonal experimental design is applied to the feature importance ranking of the influencing factors of karst tunnels. Thirdly, according to the results of the sensitivity analysis, the optimum combinations of influencing factors of tunnel construction in karst areas is obtained. The research based on these two methods shows that when taking the vertical displacement as the target variable, the parameters with the highest feature importance are A6 (tunnel diameter) and A10 (tunnel buried depth). When taking the first principal stress as the target variable, the most important influencing factors are A10 (tunnel buried depth) and A9 (location of karst cave). When taking the principal stress difference as the target variable, the most important influencing factors are A10 (tunnel buried depth) and A6 (tunnel diameter). The level combination of the 11 influencing factors obtained by taking the principal stress difference as the target variable was more balanced than the vertical displacement and the principal stress difference as the target variables. The results of this study will provide a theoretical basis to study key parameters in the response of mechanical characteristics to the safe construction of tunnels in karst areas.

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Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Influencing Factors of Karst Tunnel Construction Based on Orthogonal Tests and a Random Forest Algorithm

Wu,

Sun,

Meng

2024

Applied Sciences

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“…Currently, a number of scholars have applied finite element numerical simulation methods [9,10] as well as the limit analysis method [11,12] to study the response characteristics of the stability of the tunnel surrounding rock during tunnel excavation in karst areas. Ma et al [13] investigated the effect of the caverns on U-shaped tunnels with different cavern locations and cave shapes by numerical simulation, applying the distinct lattice spring model.…”

Section: Introductionmentioning

confidence: 99%

A Calculation Method for Reliability Index of a Deep–Bedded Karst Tunnel Construction with Cavity Located Ahead of Tunnel Working Face

Wu,

Sun,

Meng

2024

Buildings

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For the purpose of reliability quantitative assessment of the surrounding rock of the deeply embedded karst tunnel and the geological body around the cavern in the case of the cavern in the forepart of the tunnel face, on the basis of the upper bound limit analysis method, the energy dissipation theory, as well as the reliability theory, the dimensionless performance function of each damage area of the deeply buried karst tunnels could be established in the case of the cavern in the front of the tunnel face. Subsequently, the probability of failure and the reliability index of each damage region of the deep–bedded karst tunnel in the case of the cavern in the front of this tunnel face should be calculated through the Monte Carlo simulation sampling approach. The investigation has demonstrated that the larger the cohesion of the geotechnical body and the larger the internal friction angle within the geotechnical body, the larger the reliability indexes of the geotechnical bodies around the tunnel. The larger the diameter of the cavern and the larger the tunnel burial depth, the greater the probability of failure in the left part of the geotechnical body around this cavern, and the smaller the reliability indexes of these damage areas.

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“…Wang et al [20] analyzed the impact of semi-exposed karst caves of different sizes and at different positions on the stability of tunnel surrounding rock and the stress on the initial support structure through experimental methods. In the context of shield tunneling construction, scholars have used the FEM, field test, and model test to study the impact of karst caves of various sizes, spacings, and orientations on the stability of the surrounding rock during the excavation of shield tunnels [21][22][23]. Li et al [24] conducted a comprehensive geomechanical model test to examine the stability of the surrounding rock during the excavation of a shield tunnel and to uncover the causes of delayed water inrush in tunnels excavated near caves containing confined water.…”

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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Mechanical Response Law and Parameter Influence Analysis of Karst Tunnel Dynamic Excavation

Cited by 3 publications

References 20 publications

Sensitivity Analysis of Influencing Factors of Karst Tunnel Construction Based on Orthogonal Tests and a Random Forest Algorithm

Sensitivity Analysis of Influencing Factors of Karst Tunnel Construction Based on Orthogonal Tests and a Random Forest Algorithm

A Calculation Method for Reliability Index of a Deep–Bedded Karst Tunnel Construction with Cavity Located Ahead of Tunnel Working Face

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

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