Intelligent analytical method with machine learning for prediction of stratum response by shallow tunneling considering the influence of stress release

The stress release factor with the range [0, 1] is employed to reflect the stress release degree of the tunnel excavation. Elastic analytical solutions of stratum stress and displacement are obtained considering the influence of the stress release degree of the tunnel excavation based on the complex variable method. An intelligent method for determining the stress release factor is proposed through a machine learning (ML) model. Random forest (RF) is chosen as the ML model, and the sparrow search algorithm is utilized to optimize the hyperparameters of RF. The rationality of the proposed analytical method is validated by means of calculation results of FEM and field monitoring data. The influence of stress release factor and tunnel geometric parameters on the distribution laws of the stratum tensile zone and plastic zone is analyzed. The influence of tunnel stress release on stratum displacement and tunnel cross-section deformation is discussed. A dual-driven framework for the intelligent prediction of stratum displacement based on the developed analytical method is proposed and conceptually formulated. The proposed method can provide guidance in the conceptual stage of the design process of shallow tunnels.