Neural Network-Based Prediction Model for the Stability of Unlined Elliptical Tunnels in Cohesive-Frictional Soils

The scheme for accurate and reliable predictions of tunnel stability based on an artificial aeural network (ANN) is presented in this study. Plastic solutions of the stability of unlined elliptical tunnels in sands are first derived by using numerical upper-bound (UB) and lower-bound (LB) finite element limit analysis (FELA). These numerical solutions are later used as the training dataset for an ANN model. Note that there are four input dimensionless parameters, including the dimensionless overburden factor gamma D/c ', the cover-depth ratio C/D, the width-depth ratio B/D, and the soil friction angle phi. The impacts of these input dimensionless parameters on the stability factor sigma(s)/c ' of the stability of shallow elliptical tunnels in sands are comprehensively examined. Some failure mechanisms are carried out to demonstrate the effects of all input parameters. The solutions will reliably and accurately provide a safety assessment of shallow elliptical tunnels.

Automated summary

This study presents a scheme for accurate and reliable predictions of tunnel stability based on an artificial neural network (ANN). Plastic solutions of unlined elliptical tunnels in sands are derived using numerical upper-bound and lower-bound finite element limit analysis, which are then used as the training dataset for an ANN model. The study comprehensively examines the impacts of several dimensionless parameters on the stability factor of shallow elliptical tunnels in sands, and provides a reliable and accurate safety assessment.