## 4 Results Analysis
### Prediction Performance  
As shown in Figures 9 and 10, the dashed-box areas correspond to the period between December 17 and 18, 2019, during which the axial forces of the assembly steel struts with a servo system exhibit a gradual decrease. This trend can be attributed to the progressive increase in the axial-force-bearing capacity of the newly cast concrete struts. Following the casting process, the concrete struts underwent a curing period during which their strength steadily increased, enabling them to take on more of the applied load. As a result, the load previously borne by the assembly steel struts with a servo system was gradually redistributed to the concrete struts, leading to a reduction in the axial forces of the steel struts. This phenomenon is effectively captured by the spatiotemporal axial force predictions generated using the spatiotemporal adaptive network (STAN) model. As detailed in the figures, the predicted axial forces for struts ZC2-2 and ZC2-3, derived from the past 5 h of axial force data, excavation depth, and stratigraphic distribution, align well with the observed trends. Subplots (a-d) in Figures 9 and 10 compare the predicted and actual axial force values for 1 h, 2 h, 3 h, and 4 h forecasting intervals, respectively. The STAN model demonstrates high accuracy in tracking the variations in axial forces, particularly for shorter-term predictions (Figure (a) and (b)), while maintaining consistency in longer-term forecasts (Figure (c) and (d)) despite slight deviations. The alignment between the STAN model's predictions and the observed reduction in axial forces underscores its effectiveness in capturing the spatiotemporal dynamics of axial force redistribution during construction. This validates the model's applicability in monitoring and predicting the behavior of support systems under dynamic load conditions. Table 1 presents the evaluation results of the axial force predictions for the overall performance across six assembly steel struts over the next 1 to 4 h. The model's performance is quantitatively analyzed using RMSE, MAE, and R\({}^{2}\). The results show that the model achieves low errors and high prediction accuracy for forecasts. However, as the prediction time steps increase, the errors gradually grow, leading to a slight decline in accuracy. For the 1 h prediction, the RMSE is 20.77, the MAE is 14.88, and R\({}^{2}\) reaches 0.99, indicating an excellent fit between the predicted and actual values. This level of accuracy effectively meets the monitoring requirements for dynamic changes in assembly steel strut axial forces in practical engineering scenarios. The low error within the first hour demonstrates that the model effectively leverages historical data to capture both local trends and overall fluctuations in axial force variations. Figure 8: Axial force variation curves of assembly steel struts with servo system in the second layer. Figure 10: Comparison of predicted and actual axial forces of ZC2-3 for the next four hours: (**a**) next 1 st hour; (**b**) next 2 nd hour; (**c**) next 3 rd hour; (**d**) next 4 th hour. Figure 9: Comparison of predicted and actual axial forces of ZC2-2 for the next four hours: (**a**) next 1 st hour; (**b**) next 2 nd hour; (**c**) next 3 rd hour; (**d**) next 4 th hour. However, in the 2 h and 3 h predictions, the errors increase, with RMSE reaching 51.22 and 62.23, MAE rising to 38.39 and 46.60, and R\({}^{2}\) dropping to 0.93 and 0.91, respectively. Although the error growth is significant, the overall accuracy remains acceptable, and the predictions effectively capture the trends in assembly steel strut axial forces. For the longer-term 4-h prediction, the RMSE rises to 83.81, MAE increases to 59.94, and R\({}^{2}\) drops to 0.86, indicating a higher level of error. This suggests that, for longer prediction time steps, the model's ability to capture local fluctuations diminishes, although it still accurately reflects the overall trend.