Data-knowledge hybrid driven intelligent prediction method of tunnel excavation profiles geometric deformation

Applying digital twin technology to the tunnel excavation process enables intelligent tunnel construction, which is critical for ensuring construction safety and advancing intelligent building methods. However, the complex construction environment, involving numerous environmental factors and dynamic changes, poses challenges for traditional deformation prediction and analysis methods. These methods fail to systematically integrate the elements of the excavation scene to achieve accurate prediction. Therefore, this paper proposes a data-knowledge hybrid driven intelligent prediction method of tunnel excavation profile geometric deformation. A dynamic data-driven tunnel excavation knowledge extraction method is designed, along with the construction of a multi-process coupled tunnel excavation knowledge base. Additionally, a deep learning-based excavation profile deformation prediction model is developed, integrating the data-knowledge hybrid driven method to improve prediction performance. The experimental results demonstrate that the method proposed in this paper can significantly integrate and analyse the deformation elements of the tunnel excavation scene. It achieves intelligent prediction of the tunnel excavation profile geometric deformation, with reductions of 57.46%, 76.00%, and 53.12% in MAE, MSE, and RMSE, respectively, compared with the traditional prediction model. This method effectively enhances the deformation prediction accuracy of the tunnel excavation profile twin model.