# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 4 Results
### Intelligent Control of Exacavation-Induced Wall Deflection  
Based on the previous analysis of the prediction results and the visualization of the connections between different features and nodes, the effectiveness of the proposed wall deflection prediction model has been validated. The prediction results, combined with a servo support system, are input to a PID controller, developing an intelligent control system for wall deflection. Specifically, P refers to the proportional component, which adjusts the control output proportionally to the current error. I represents the integral component, which accounts for past errors to eliminate steady-state offset; and D is the differentiation component, which predicts future error trends by considering the rate of change, facilitating the stabilization of the system and reducing overshooting. For most tasks, using the P and I components is sufficient to meet control requirements [52; 53]. Therefore, this study considers wall deflection under different kp and ki settings. kp refers to the proportional gain, while ki is the integral gain. Figure 17 shows wall deflection control when kp is set as 0.01, 0.8, and 0.9. In these cases, ki is set as 0. The blue dashed line represents the target deflection values, which are set as 7.5 mm, 9.6 mm, 17 mm, and 30 mm for monitoring points 2, 3, 4, and 6, respectively. The results indicate that as the kp value increases, the control model responds more rapidly. Specifically, the adjusted wall deflection converges  
Figure 16: Feature attribution map across monitoring points. Figure 15: Attention weights between various features. toward the target deflection faster with higher kp values. This trend is evident from the graphs, where closer alignment with the target deflection lines (blue dashed) is observed as kp increases from 0.01 to 0.9. However, small errors still remain in the adjusted wall deflection at points 2 and 3, while larger errors persist at points 4 and 6. This discrepancy suggests that the integral component should be further incorporated into the control model. The effects of adjusting the ki settings on wall deflection are presented in Figure 18. The error at ki = 0.01 is significantly larger than those at ki = 0.1 and 0.2. Both ki = 0.1 and 0.2 reach the target deflection values after about 18 days of adjustment with ki = 0.2 showing better results at deflection points 4 and 6. Although a slight overshoot occurs at wall deflection point 3, with the deflection exceeding the target by less than 0.3 mm, it quickly stabilizes within one to two days. The results indicate that by properly setting ki and kp, the proposed intelligent control system can effectively control wall deflection, keeping it close to the target deflection. When evaluating the proposed wall deflection control system, it is necessary to consider whether the axial forces are reasonable. Excessively high or low axial forces in the steel supports can affect the stability of the support system. High axial forces may cause the supports to fail or excessively deform, while low forces may provide insufficient support, both of which can lead to structural failure. Figure 19 shows the comparison of axial forces before and after adjustment. The reasonable range is determined by the historical maximum and minimum values of these steel supports. The axial force ranges for the steel supports are as follows: 2000 kN to 3000 kN for support 2, 3000 kN to 4000 kN for support 3, and 4000 kN to 5200 kN for supports 4 and 6. The results show that the adjusted axial forces for all four steel supports fall within the reasonable range, demonstrating the effectiveness of the proposed model. In addition, the proactive control system contributes significantly to the safety and cost-effectiveness of excavation projects. By enabling predicted monitoring and the adjustment of axial forces, the system facilitates the prevention of support failure or structural instability, reducing the risk of accidents and associated costs.