## 4 Analysis and Discussions
### Limitations and Development Trends  
On the basis of the above analysis, we find that in the field of subsidence prediction during subway tunnel construction, the SSA-VMD neural network model has high prediction accuracy and applicability and is able to effectively address complex nonlinear and nonsmooth time series data in the construction process of subway tunnels, but it has certain limitations. The limitations of the SSA-VMD neural network model for settlement prediction during subway tunnel construction are first reflected in the data requirements. The model is highly dependent on high-quality data inputs; however, there are many challenges to data acquisition in an underpass tunnel construction environment. The complexity and uncertainty of the construction site make the data collection susceptible to various interferences, such as dynamic changes in the water table, the impact of loads on surrounding buildings, and the vibration of construction machinery, which can lead to the presence of noise and outliers in the collected data, which can adversely affect the accuracy of the model. Second, the determination of the parameters of the model is more \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Groups** & **Normalcy** & \begin{tabular}{c} **Full-Cycle** \\ **Yellow Alert** \\ \end{tabular} & \
\begin{tabular}{c} **Early Warning of Mid-** \\ **Settlement Phase** \\ \end{tabular} &
\begin{tabular}{c} **Early Warning** \\ **Error** \\ \end{tabular} \\ \hline Original & 134 & 104 & 9 & 0 \\ GRU & 143 & 96 & 0 & 9 \\ LSTM & 145 & 93 & 0 & 11 \\ RNN & 138 & 100 & 0 & 14 \\ SSA–VMD-GRU & 139 & 99 & 5 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Risk prediction results of the models. complicated. The SSA-VMD neural network model involves several parameters, including the number of modes in the VMD, the penalty factor, etc., as well as the number of layers of the neural network, the number of nodes, and the learning rate. In the prediction of subway tunnel construction settlement, the selection of these parameters often lacks a uniform standard and needs to be determined on the basis of experience or repeated tests, which is not only time-consuming and labor-intensive but also may not be able to obtain the optimal parameter configuration in different engineering scenarios, which in turn affects the prediction effect of the model. Furthermore, the computational cost of the model is relatively high. Since the settlement data during the construction period of subway tunnels are characterized by long time series and large data volumes, a large amount of computational resources and time are required for SSA decomposition, VMD, and the training and prediction of neural networks. This limits the real-time prediction ability of the model to a certain extent, which is not conducive to timely decision-making during the construction process. In addition, there are several problems with the generalizability of the model. The geological conditions, construction processes, and surrounding environments in which subway tunnels are constructed vary widely, and the prediction model obtained by the SSA-VMD neural network model trained in one engineering scenario may have prediction bias in other scenarios because it is difficult for the model to adequately consider all the differentiating factors of different subway tunnel constructions. Accordingly, the SSA-VMD neural network model has a positive development trend in the prediction of settlement during the construction period of subway tunnels. In terms of data acquisition, with the continuous development of sensor technology, high-precision and anti-interference data acquisition equipment will continue to emerge, which can obtain more accurate and stable settlement data and provide reliable inputs for the model. Moreover, advanced data preprocessing techniques, such as wavelet transforms and other methods, can be applied to process noise-containing data and improve data quality.