## 2 Methods
### Fusion of Safety Performance Parameters and BP Neural Network
#### 2.1.3 Fusion of Safety Parameters  
By analyzing the basic principle of the BP neural network, it provides a new idea for intelligent prediction of prestressed steel structure construction safety. In the process of structural safety prediction, the finite element software is used for simulation analysis [31]. Firstly, the design parameters of the structure are selected and the construction conditions of the structure are adjusted to obtain the corresponding mechanical parameters. A large number of samples are formed during finite element analysis. The sample contains the design parameters and mechanical parameters of the structure, which provides data support for the intelligent prediction model of structural construction safety based on BP neural network. According to the characteristics of prestressed steel structure, the aggregate of structural design parameters and mechanical parameters selected in this study is specifically expressed as Equations (4) and (5):  
\[DP=(D_{ucb},\ D_{s},\ D_{c},\ N_{s},\ A_{c},\ IT_{c}) \tag{4}\]  
\[MP=\big{(}D_{p},\ S_{c}\big{)}. \tag{5}\]  
In the Equation, \(DP\) represents the set of structural design parameters. \(D_{ucb}\) equals to the size of the upper chord beam in the structure. \(D_{s}\) means the size of the strut. \(D_{c}\) is the diameter of the lower chord cable. \(N_{s}\) represents the root number of struts in the structure. \(A_{c}\) denotes the radian of the lower chord cable to determine the length of the strut. \(IT_{c}\) represents the initial tension of the lower chord cable. \(MP\) is the set of structural mechanical parameters, \(D_{p}\) represents the maximum vertical displacement of the structure, and \(S_{c}\) equals to the stress of the lower chord cable. The selection of design parameters provides the basis for building the simulation model, and the selection of mechanical parameters provides the basis for the feasibility of structural safety prediction.  
In the construction safety prediction of prestressed steel structure, the initial parameters of the shape-finding model are determined first, including the number of nodes in the input layer, output layer, and hidden layer. In this process, based on the characteristics of structure construction, the type number of design parameters is selected as the number of input layer neurons, and the type number of mechanical parameters is selected as the number of output layer neurons. Before the prediction model is established, the simulation model is formed based on the selected design parameters to obtain the finite element results. The data from the finite element analysis are used as the training samples of the prediction model. By adjusting the network parameters, the error between the predicted value and finite element results is guaranteed to be within the threshold. Thus, the intelligent prediction model of structural construction safety is formed based on the BP neural network. In this model, the mechanical parameters of the structure can be accurately calculated and predicted by inputting design parameters. Based on the above content, the establishment of the intelligent prediction model of structural construction safety based on the BP neural network is shown in Figure 2.