## 4 Results Analysis
### Method Comparison
This superiority arises from the STAN method's spatiotemporal adaptive mechanism, which effectively integrates temporal features, spatial characteristics, and geological information, enabling a more comprehensive capture of the dynamic patterns in assembly steel strut axial forces. In long-term predictions, the STAN method mitigates the impact of error accumulation by leveraging joint modeling of temporal dependencies and spatial distribution characteristics, thereby enhancing the robustness of the model. The LSTM method performs well in short-term and mid-term predictions and significantly outperforms the traditional RNN method. By introducing a gating mechanism, LSTM captures temporal dependencies in time-series data more effectively, demonstrating stronger modeling capability for complex temporal patterns. For instance, in the 1 h and 2 h predictions, while the performance differences among the methods are relatively minor, \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Next 1 st Hour**} & \multicolumn{3}{c}{**Next 2 nd Hour**} & \multicolumn{3}{c}{**Next 3 rd Hour**} & \multicolumn{3}{c}{**Next 4 th Hour**} \\ \cline{2-13}  & **RMSE** & **MAE** & **R2** & **RMSE** & **MAE** & **R2** & **RMSE** & **MAE** & **R2** & **RMSE** & **MAE** & **R2** \\ \hline RNN & 30.47 & 22.48 & 0.96 & 60.58 & 48.37 & 0.87 & 94.48 & 74.18 & 0.71 & 127.03 & 99.25 & 0.62 \\ LSTM & 28.04 & 20.83 & 0.97 & 56.71 & 42.78 & 0.92 & 91.42 & 66.86 & 0.81 & 124.74 & 95.74 & 0.67 \\ GRU & 27.41 & 21.04 & 0.98 & 53.09 & 42.45 & 0.93 & 84.99 & 68.03 & 0.73 & 123.02 & 91.94 & 0.65 \\ STAN & 20.77 & 14.88 & 0.99 & 51.22 & 38.39 & 0.93 & 62.23 & 46.60 & 0.91 & 83.81 & 59.94 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of overall predicted axial force performance for assembly steel struts with servo system using various methods. LSTM achieves slightly lower RMSE and MAE values than RNN, indicating that its gating mechanism effectively improves trend fitting for assembly steel strut axial forces. The GRU method, similar to LSTM, incorporates a gating mechanism to manage long-term dependencies while offering a more simplified architecture with fewer parameters. It demonstrates comparable performance to LSTM in short-term and mid-term predictions, achieving slightly better accuracy in longer time steps due to its improved generalization ability. However, like LSTM, it remains limited in its ability to fully capture local fluctuations over extended time horizons. However, a shared limitation of the LSTM, GRU, and RNN approaches is their sole focus on the temporal relationships of assembly steel strut axial forces without considering the spatial correlations between struts. In the 3 h and 4 h predictions, this limitation significantly reduces their ability to capture local fluctuation characteristics, causing the predicted results to deviate substantially from the actual trends. In contrast, the STAN method comprehensively integrates temporal features, spatial characteristics, and geological information, enabling more holistic modeling of the dynamic variations in assembly steel strut axial forces. By capturing both the overall trends and local details, the STAN method significantly outperforms the LSTM, GRU, and RNN methods. Its performance advantage becomes even more pronounced in long-term predictions, showcasing remarkable adaptability and robustness.