Machine learning approaches for stability prediction of rectangular tunnels in natural clays based on MLP and RBF neural networks

In underground space technology, the issue of tunnel stability is a fundamental concern that significantly causes catastrophe. Owing to sedimentation and deposition processes, the strengths of clays are anisotropic, where the magnitudes of undrained shear strengths in the vertical and horizontal directions are different. The anisotropic undrained shear (AUS) model is effective at considering the anisotropy of clayey soils when analyzing geotechnical stability issues. This study aims to assess the stability of rectangular tunnels by adjusting the dimensionless overburden factor, cover-depth ratio, and width-depth ratio in clay with various anisotropic strength ratios. The stability analysis of these tunnels involves employing finite element limit analysis and the AUS model to identify the planes of soil collapse in response to the aforementioned variations. In addition, this study presents the development of soft-computing models utilizing artificial neural networks (ANNs) to forecast the stability of rectangular tunnels across various combinations of input parameters. The findings of this study are presented in the form of design charts, tables, and soft-computing models to facilitate practical applications

Stability evaluation of elliptical tunnels in natural clays by integrating FELA and ANN

The stability of tunnels in clayey soil is a major concern for underground space technology. Clay has anisotropy in shear strength induced by depositional and sedimentation processes. For the numerical analysis of geotechnical stability problems, the anisotropic undrained shear (AUS) model can account for this anisotropy of clayey soils. In this study, the stability of the elliptical tunnel (stability factor: σs-σt/suc) with varying elliptical shape (width-depth ratio: B/D) placed at different embedment depths (cover-depth ratio: C/D) in clay with different anisotropy (anisotropic strength ratio: re) and varying dimensionless overburden factor (overburden factor: γD/suc) is evaluated using finite element limit analysis and the AUS model. The failure planes are also evaluated for the above variations. Based on the numerical outcome, the artificial neural network (ANN) is utilized to establish the equation for predicting the stability of the elliptical shape tunnel with different shapes (i.e., width-depth ratio), and varying overburden, cover-depth ratio, varying anisotropic strength ratio of clay. The present study results are presented as design charts, tables, and equations so that they can be used in practice