## 1 Introduction
Numerous researchers domestically and internationally [8; 9; 10] have pioneered key construction technologies by modifying shield machine construction methods and overcoming the technical difficulties of shield machine construction under diverse geological conditions. Regarding numerical simulation and analysis, [PERSON] et al. [11] used the FEM-based software ABAQUS to analyse the deformation of specific rock mass characteristics under static and seismic conditions with and without lining. [PERSON] et al. [12] used MIDAS GTS NX to carry out 3D finite element numerical simulations to optimize the geometric parameters and construction sequence of twin tunnels to help designers control the settlement caused during the excavation of back-loaded twin tunnels. [PERSON] et al. [13] suggested a modelling method based on the coupled finite difference and discrete element methods, simulating the interaction between the shield machine and the layered rock mass. They further discussed the progressive failure mechanism of a layered rock mass. [PERSON] et al. [14] developed an artificial neural network model that considered the non-linear relationship between the maximum surface settlement and 150 influential independent variables and collected real data from the Tehran Metro Line 16 project to build a training and test set to optimize the ANN technique, which eventually predicted the surface settlement of the above project accurately. [PERSON] et al. [15] used the open-source AutoML framework to construct different machine-learning models to predict the maximum ground settlement when shield tunnels are constructed on soft subsoil, with advantages in terms of prediction accuracy. [PERSON] et al. [16] adopted four machine learning (ML) algorithms and four deep learning (DL) algorithms in shield machine attitude (SMA) prediction models, and finally integrated ML algorithms and DL algorithms to design a warning predictor for SMA according to the eight simulation results. [PERSON] et al. [17] formulated a hybrid prediction dataset that incorporated geological and tectonic parameters. They based this on sampling methods using spatial and time series to obtain an approximate range of subsidence to reduce the potential damage the project might inflict on the surrounding environment. [PERSON] et al. applied the Matlab program of the BP neural network based on a genetic algorithm combined with engineering examples to achieve relatively efficient construction feedback through a forward analysis of construction parameters, which can effectively improve the response efficiency of unexpected conditions in the construction process. Many scholars [18; 19; 20; 21; 22; 23; 24] have used finite element numerical simulation, machine learning method comparison, and genetic algorithms to perform the numerical modelling of shield tunnelling under special external loads, investigating the deformation characteristics and mechanisms of tunnel structures. The results of the digital modelling are used to predict changes in construction conditions, optimize construction parameters, and control construction quality. In summary, domestic research on shield machine empty-pushing through mining method in tunnel construction has primarily focused on aspects such as construction technology, quality control, and monitoring and measurement techniques. However, research contributions in structural stress and design are somewhat limited. In practical scenarios, as tunnelling projects progress, frequent stress disturbances occur within the structure. This results in substantial changes in the internal forces of the tunnel structure, which often fluctuate under varying building conditions. During the construction of a mine method tunnel project, the internal force fluctuations can exceed their limits, leading to potential failure or even collapse, of the existing structure. This poses risks to buildings surrounding metro lines and complicates tunnel construction. Ensuring safety through excessive building measures would inflate the project's cost.