## 4 Analysis and Discussions
### Mode Decomposition Algorithm-Recurrent Neural Network Combined Prediction Model
As CEEMDAN overcomes the endpoint effect, the prediction accuracy remains stable in the later stages shown in Figure 12, but the addition of white noise leads to insufficient learning of the data in the pretraining set and results in many volatility errors in the prediction curves. The data from the VMD are significantly better than those from the EMD in terms of regularity, as shown in Figure 12b. When a single neural network is used for prediction, the GRU reasonably fits the fluctuation data in fluctuation periods one to three. However, the combined SSA-VMD-GRU prediction model not only improves the prediction accuracy for the trend in the fluctuation period but also predicts the sudden changes in the short-term data more accurately. The volatility periods show that the SSA-VMD-GRU model maintains a good fit for the nonlinearly changing data and outputs nonlinear data; however, there are limitations in predicting data in the later stabilization phase and the prediction error becomes larger. better than that of CEEMDAN in terms of stability. On the other hand, VMD achieves the second highest prediction accuracy after CEEMDAN but is much better than CEEMDAN in terms of stability, and the prediction accuracy and stability of EMD and WPT are not ideal. Figure 12: A comparison of the prediction results of each combined decomposition model. (**a**) Combinations of RNNs with different decomposition algorithms. (**b**) Combinations of LSTM with different decomposition algorithms. (**c**) Combinations of the GRU with different decomposition algorithms. In terms of short-term volatility details, the combined model of VMD and the neural network has greater consistency with the actual data in terms of the forecasting results, which is significantly better than the forecasting results of a single neural network model or other neural networks alone. This suggests that the SSA-VMD-GRU model is more accurate in capturing short-term fluctuation characteristics and can therefore be used for short-term warnings in real-time construction. Taking the single-day measured data of the DB1-17 monitoring point as an example, the real-time data of the YG1-17 monitoring point are usually analyzed via the SSA-VMD-GRU model for single-day monitoring and warning in the actual construction process. Combined with the Urban Railway Transportation Engineering Specification GB 50911-2013 [46], the construction site is usually analyzed on the basis of the real-time settlement and settlement rate, especially through the settlement rate, to determine the construction safety condition and accordingly develop a reasonable onsite monitoring program. The SSA-VMD-GRU model provides an effective tool for real-time monitoring, enabling a real-time prediction and early warning of potential risks in dynamic construction environments and helping to improve construction safety and efficiency. Therefore, when the cumulative settlement and settlement rates both reach 70% of the control value or when one reaches 85% of the control value, a yellow warning is issued to prompt the construction crew to pay attention to the current situation to avoid further settlement problems. The installation of yellow warnings helps the crew take early action to prevent the problem from worsening when the settlement is still manageable. If the cumulative settlement and rate of settlement both reach 85% of the control value or one of them reaches that value, an orange warning is issued, indicating that the construction environment is approaching a critical state and that enhanced monitoring and urgent intervention are needed to prevent safety hazards from occurring. Finally, when the cumulative settlement and settlement rates both reach the control value, a red warning is issued, indicating that serious problems may have occurred during the construction process and that the construction must be stopped immediately and that appropriate safety measures must be taken to ensure the safety of construction personnel and the smooth progress of the project.