Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection - Weiwei Liu; Shaoxiang Zeng; Kaiyue Chen; Xiaodong Pan (Applied Sciences)
The advancement of urbanization has led to stricter requirements for the prediction and control of excavation-induced deformations. To achieve this goal, this study proposes a novel method that integrates a spatiotemporal graph attention network (ST-GAT) with a proportional–integral–derivative (PID) controller to proactively control wall deflections caused by excavation. The ST-GAT model improves wall deflection prediction by capturing spatial relationships between monitoring points near steel struts and dynamically assigning weights based on their importance. The interpretability of the model is greatly improved by generating a feature attribution map across various input features and visualizing the weight distribution between nodes in the GAT network. A proactive control method of wall deflections is proposed by replacing current monitoring values in the PID control system with predicted values for multiple steel struts using the ST-GAT model. Compared to the standard PID method, this approach can control wall deflections before significant deformations occur. A real excavation project equipped with a servo support system is used to validate the effectiveness of the proposed method. The results show that the ST-GAT model significantly outperforms other models, and its performance improves when utilizing spatial relationships from more monitoring points. With a reasonable combination of PID hyperparameters, the proposed ST-GAT-based PID controller can control wall deflections close to a target value.