## 3 Results
### Establishment of Prediction Model  
According to the analysis of the basic principle of the BP neural network in Section 2.1.2, an intelligent prediction model for prestressed steel structure construction safety is established. The design parameters and mechanical parameters are used as the input layer and output layer of the neural network, respectively. The data obtained by the finite element under the self-weight condition of the structure are used as the samples of the training prediction model. In this study, a hidden layer was established, in which the number of neurons in the hidden layer was determined by Equation (7) [35]:  
\[l=\sqrt{n+m}+a. \tag{7}\]  
Among them, \(l\) is the number of hidden neurons. \(n\) is the number of neurons in the input layer, namely the number of types of design parameters. \(m\) is the number of neurons in the output layer, namely the number of types of mechanical parameters. \(a\) is the constant between [1, 10]. The number of iterations of the BP neural network is 6000, and the learning rate is 0.035. The model selects _mse_ as the performance function of the evaluation model, and ends the iteration when \(mse\leq 6.5\times 10^{-4}\). In the construction process of this structure, in order to accurately predict the mechanical parameters, two prediction models of the vertical displacement and stress of the lower chord cable corresponding to the design parameters are established, respectively. According to engineering experience, the values of six design parameters are shown in Table 1.  
According to the value of design parameters, 150 samples are formed under the condition of self-weight. In the process of training the prediction model, the influence of the number of neurons in different hidden layers on the number of iterations is discussed. When \(mse\leq 6.5\times 10^{-4}\), the model stops iteration. In this study, the effects of six types of design parameters on the vertical displacement and stress of the lower chord cable are studied respectively. Therefore, in each neural network, \(n=6\), \(m=1\). According to Equation (7) in each neural network, \(l\) takes 10 integers between 4 and 13. The effect of the number of neurons in different hidden layers on the number of iterations is shown in Figure 7.  
In the process of neural network training, the less the number of iterations, the higher the efficiency of calculation. At the same time, the corresponding prediction accuracy is also higher. According to Figure 7, for the prediction of vertical displacement and lower chord cable stress, the number of hidden layer nodes respectively takes 11 and 6 corresponding to the least number of iterations, and the accuracy of the model is the highest. Thus, the number of nodes corresponding to the neural network for different mechanical parameters in the prediction of structural safety performance is determined. The comparison between the predicted values (PV) of mechanical parameters output by the final prediction model and the actual values (AV) of mechanical parameters at the construction site are shown in Figures 8 and 9. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Type** & **Taking Values** \\ \hline Size of upper chord beam (mm) & H700 \(\times\) 250 \(\times\) 20 \(\times\) 36, H700 \(\times\) 250 \(\times\) 16 \(\times\) 20 \\ Size of strut (mm) & \(\varnothing\)150 \(\ast\) 8, \(\varnothing\)160 \(\ast\) 8, \(\varnothing\)170 \(\ast\) 8 \\ Diameter of lower chord cable (mm) & 50, 55, 60, 65, 70 \\ Number of the strut & 7, 9, 11 \\ Arc of lower chord cable (\({}^{\circ}\)) & 8, 9, 10 \\ Initial tension of the lower chord cable (KN) & 600, 700, 800, 900, 1000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Values of design parameters. Figure 8: Comparison of the vertical displacement output from the prediction model with the actual vertical displacement (part of the samples). Figure 7: Effect of different number of hidden layer neurons on the iteration number.