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<p>Tunnel linings are critical for enhancing underground structure stability, yet their role in tunnel stability assessments remains underexplored in conventional analysis frameworks. This study addresses this gap by evaluating tunnel stability under surcharge loading conditions, specifically examining single and dual horseshoe-shaped tunnel configurations using finite element limit analysis. Two dimensionless stability factors are introduced: <i>N</i><sub><i>s</i></sub>, representing the ratio of collapse multiplier to uniaxial compressive strength for unlined tunnels, and <i>N</i><sub><i>i</i></sub>, the tunnel stability improvement factor, is defined as the ratio of the loads required to cause collapse of lined tunnels to those required for unlined tunnels. The variations in <i>N</i><sub><i>i</i></sub> and <i>N</i><sub><i>s</i></sub> are investigated through a detailed parametric analysis of key factors, including cover depth ratio (<i>δ</i>), horizontal tunnel spacing (<i>μ</i>), and lining thickness (<i>β</i>). The results show that the unlined tunnels exhibit severely compromised stability, with critical thresholds of <i>N</i><sub><i>s</i></sub> &lt; 0.6 for single tunnels and <i>N</i><sub><i>s</i></sub> &lt; 0.35 for dual tunnels at <i>μ</i> = 2, underscoring the necessity of structural support. The stability improvement factor (<i>N</i><sub><i>i</i></sub>) increases with greater <i>δ</i> and <i>μ</i>, enabling optimization of the required lining thickness (<i>β</i>). The analysis also reveals three distinct potential slip-plane patterns in the surrounding rock mass for both configurations, confirming that lining thickness, cover depth, and tunnel spacing significantly influence failure mechanism. Furthermore, predictive regression models based on Optimizable Gaussian Process Regression, Wide Neural Network, and Optimizable Ensemble techniques demonstrate robust performance in forecasting <i>N</i><sub><i>s</i></sub> and <i>N</i><sub><i>i</i></sub>.</p>

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Numerical Investigation and Design Recommendations for Steel Linings in Single and Multiple Tunnels Under Surcharge Loading

  • 期刊论文

  • 发表日期：

    2025年9月26日

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  • Aayush Kumar,
  • Vinay Bhushan Chauhan

摘要

Tunnel linings are critical for enhancing underground structure stability, yet their role in tunnel stability assessments remains underexplored in conventional analysis frameworks. This study addresses this gap by evaluating tunnel stability under surcharge loading conditions, specifically examining single and dual horseshoe-shaped tunnel configurations using finite element limit analysis. Two dimensionless stability factors are introduced: Ns, representing the ratio of collapse multiplier to uniaxial compressive strength for unlined tunnels, and Ni, the tunnel stability improvement factor, is defined as the ratio of the loads required to cause collapse of lined tunnels to those required for unlined tunnels. The variations in Ni and Ns are investigated through a detailed parametric analysis of key factors, including cover depth ratio (δ), horizontal tunnel spacing (μ), and lining thickness (β). The results show that the unlined tunnels exhibit severely compromised stability, with critical thresholds of Ns < 0.6 for single tunnels and Ns < 0.35 for dual tunnels at μ = 2, underscoring the necessity of structural support. The stability improvement factor (Ni) increases with greater δ and μ, enabling optimization of the required lining thickness (β). The analysis also reveals three distinct potential slip-plane patterns in the surrounding rock mass for both configurations, confirming that lining thickness, cover depth, and tunnel spacing significantly influence failure mechanism. Furthermore, predictive regression models based on Optimizable Gaussian Process Regression, Wide Neural Network, and Optimizable Ensemble techniques demonstrate robust performance in forecasting Ns and Ni.