Machine learning-based investigation of uplift resistance in special-shaped shield tunnels using numerical finite element modeling

The uplift resistance of the soil overlying shield tunnels significantly impacts their anti-floating stability. However, research on uplift resistance concerning special-shaped shield tunnels is limited. This study combines numerical simulation with machine learning techniques to explore this issue. It presents a summary of special-shaped tunnel geometries and introduces a shape coefficient. Through the finite element software, Plaxis3D, the study simulates six key parameters—shape coefficient, burial depth ratio, tunnel’s longest horizontal length, internal friction angle, cohesion, and soil submerged bulk density—that impact uplift resistance across different conditions. Employing XGBoost and ANN methods, the feature importance of each parameter was analyzed based on the numerical simulation results. The findings demonstrate that a tunnel shape more closely resembling a circle leads to reduced uplift resistance in the overlying soil, whereas other parameters exhibit the contrary effects. Furthermore, the study reveals a diminishing trend in the feature importance of buried depth ratio, internal friction angle, tunnel longest horizontal length, cohesion, soil submerged bulk density, and shape coefficient in influencing uplift resistance.