## 5 Conclusions  
This study proposes a spatiotemporal-adaptive-network-based method for predicting the dynamic variations in axial forces of assembly steel struts with a servo system during foundation pit excavation. The following conclusions are drawn:  
1. The STAN model integrates the self-attention mechanism with a convolutional neural network, effectively combining temporal features, spatial characteristics, and geological information. This integration significantly enhances the model's ability to capture and predict the complex dynamic variations in axial forces of assembly steel struts with a servo system. 2. The STAN model was validated using monitoring data of axial forces from assembly steel struts with a servo system in an actual engineering project. The results demonstrate that the STAN model effectively captures the dynamic variation trends of axial forces in both short-term and long-term predictions, with the predicted curves closely aligning with the actual values. In short-term predictions, the model excels at accurately capturing local fluctuation characteristics. For long-term predictions, despite the increased challenges posed by longer time steps, the STAN model continues to effectively reflect the overall trends, showcasing strong stability and adaptability. 3. The comparative analysis with the RNN, LSTM, and GRU methods demonstrates that the STAN model offers significant advantages in capturing the dynamic variation patterns of axial forces in assembly steel struts with a servo system. Particularly in long-term predictions, the STAN model, through joint modeling of spatiotemporal features, provides a more comprehensive reflection of the overall trends and local fluctuation characteristics. In contrast, the RNN, LSTM, and GRU methods, which only consider temporal relationships and fail to incorporate spatial features, exhibit a gradual decline in prediction accuracy as the prediction time steps increase. While the GRU approach performs similarly to the LSTM method and achieves slightly better accuracy in long-term predictions due to its improved generalization ability, both methods still struggle to fully capture local fluctuations and spatial dependencies. This further validates the adaptability and stability of the STAN model in complex engineering environments, demonstrating its superiority in effectively predicting axial force variations in deep excavation projects. Fluctuations in axial forces of assembly steel struts with a servo system are critical factors affecting the stability of foundation pit support systems, with abnormal variations potentially leading to structural instability and severe safety incidents. Accurate prediction and effective early warning of axial forces are therefore essential for ensuring construction safety and reducing the risk of accidents during foundation pit excavation. The proposed STAN model achieves precise modeling and prediction of dynamic changes in axial forces of assembly steel struts with a servo system, providing technical support for axial force early warning during construction. This method has theoretical significance and offers a potential solution for early warning in practical engineering, serving as a reference for related research and engineering applications. Future research will explore the integration of physical constraints associated with temperature fluctuations regarding neural network frameworks to further enhance the accuracy of long-term axial force predictions. This will be achieved by embedding key thermal-mechanical principles and material behavior, particularly the effects of temperature-induced expansion and contraction of steel supports, directly within the model architecture. The enhanced hybrid approach is expected to better capture the complex interactions between temperature variations and axial force dynamics.