## 4 Results Analysis
### Method Comparison  
To demonstrate the superiority of the proposed STAN method in predicting assembly steel strut axial forces, a comparative analysis was conducted against the RNN method proposed by [PERSON] [26], the LSTM method proposed by [PERSON] et al. [1], and the GRU method proposed by [PERSON] et al. [27]. As shown in Figures 11 and 12, the performance of each method was evaluated by comparing the predicted and actual axial forces of assembly steel struts ZC2-2 and ZC2-3 over the next 1, 2, 3, and 4 h. These comparisons highlight the performance of each method across different prediction time steps. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Predicted Axial Force** **(Future Hours)**} & \multicolumn{3}{c}{**Evaluation Metrics**} \\ \cline{2-4}  & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline Next 1 st Hour & 20.77 & 14.88 & 0.99 \\ Next 2 nd Hour & 51.22 & 38.39 & 0.93 \\ Next 3 rd Hour & 62.23 & 46.60 & 0.91 \\ Next 4 th Hour & 83.81 & 59.94 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of overall predicted axial force performance for assembly steel struts with servo system over the next four hours. Figure 11: Comparison of the performance of different methods in predicting axial forces of ZC2-2 over 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 12: Comparison of the performance of different methods in predicting axial forces of ZC2-3 over the next four hours: **(a)** next 1 st hour; **(b)** next 2 nd hour; **(c)** next 3 rd hour; **(d)** next 4 th hour. The figures reveal significant differences in the ability of the three methods to predict assembly steel strut axial forces. The RNN method captures the overall trend of the axial force variations but responds more slowly to regions with rapid fluctuations and shows limitations in fitting local details. As the prediction time steps increase, the RNN method's capacity to model complex dynamic changes becomes increasingly constrained, making it difficult to maintain stable performance in long-term predictions. The LSTM method, by incorporating a gating mechanism, effectively captures temporal dependencies in the time series. Compared to RNN, it demonstrates improved performance in short-term predictions, particularly excelling in capturing rapid fluctuations. As the prediction time steps increase, LSTM maintains relatively stable performance in modeling global trends, but its fitting capability in regions with pronounced local fluctuations remains somewhat limited. The GRU method, similar to LSTM, also leverages gating mechanisms to manage long-term dependencies while reducing computational complexity. It achieves comparable performance to LSTM in predicting axial force variations but exhibits slightly better generalization in long-term forecasts, capturing local fluctuations more effectively. However, like LSTM, it still faces challenges in maintaining high accuracy under the influence of temperature fluctuations. In comparison, the STAN method demonstrates significant advantages in both short-term and long-term predictions. For the 1 h and 2 h predictions, the STAN method accurately captures the overall trend of the assembly steel strut axial forces while effectively characterizing the local fluctuation features. The predicted curves align closely with the actual values, reflecting its high precision. For the longer-term 3 h and 4 h predictions, despite the increased demands placed on the model due to the increase in prediction time steps, the STAN method continues to effectively capture the overall trends and maintains a high degree of consistency in regions with local fluctuations. This highlights its strong adaptability and stability across different prediction time steps. The quantitative analysis in Table 2 highlights the significant advantages of the STAN method in predicting assembly steel strut axial forces, particularly in handling long-term predictions, where its performance far surpasses that of the other methods.