Stability Design Charts and Equations for Rectangular Tunnels Using Terzaghi’s Modified Stability Factors and Machine Learning

Stability Design Charts and Equations for Rectangular Tunnels Using Terzaghi’s Modified Stability Factors and Machine Learning

The objective of this study is to investigate the stability of plane strain rectangular tunnels under the effects of soil cohesion, surcharge loading, and soil unit weight. The novelty of the study is to extend Terzaghi's bearing capacity equation approach for determining three tunnel stability factors (Nc, Ns, and Nγ) that can be used to evaluate a tunnel's stability. These stability factors, functions of the coverdepth ratio (H/D), width-to-height ratio (B/D), and drained friction angle (ϕ), are employed in conjunction with the principle of superposition to assess the overall stability of a tunnel. To achieve this objective, the study employs finite-element limit analysis (FELA) with adaptive meshing techniques to ensure accurate calculations and address any disparities between the upper bound and lower bound solutions. The analysis elucidates the failure mechanisms of the tunnel and validates the results by comparing them with prior research. In addition, closed-form equations are developed to facilitate the calculation of these stability factors using machine learning methods, consisting of artificial neural network and support vector machine. The research is expected to provide valuable insights into the stability of rectangular tunnels, particularly in scenarios involving soil cohesion, surcharge loading, and varying soil unit weights. The combination of traditional geotechnical principles with modern FELA numerical methods and machine learning predictive models promises to offer a comprehensive and practical approach for tunnel stability analysis.

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