# Incomplete Similarity Error Analysis Method of Steel Frame Structure Based on Correlation
## 4 Discussion
### Prediction of Similarity Error of Semi-rigid Steel Frame  
This section uses the trained neural network in Section 4 to predict the similarity error of the steel frame and test the prediction accuracy of the neural network. Take the same three-layer three-span steel frame structure, the basic parameters of the structure are shown in Table 7.  
Two types of commonly used H-beams with beam heights of 100 and 125 were chosen. From former analysis, it can be seen that the plate thickness contributes less to the similarity error, so the plate thickness difference is allowed to be larger and more in line with engineering practice. According to the three factors that have a great influence on the sensitivity analysis--which are the columns' height, the strong-axis stiffness of beam-column connections and the stiffness of column base--the similarity error of beams' height is 25% and the similarity error of strong-axis stiffness of beam-column connections is 56.25%. The similarity error of weak-axis stiffness of column base is 56.25%. After modeling and  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Components** & **Parameters** & **Value** & \
\begin{tabular}{c} **Similar** \\ **Error** \\ \end{tabular} & **Components** & **Parameters** & **Value** &
\begin{tabular}{c} **Similar** \\ **Error** \\ \end{tabular} \\ \hline \multirow{4}{*}{\(\text{B}_{\text{S}}\)} & H & 100 & 25\% & & H & 100 & 0\% \\  & W & 50 & 25\% & & W & 50 & 0\% \\  & T\({}_{\text{W}}\) & 5 & 67.5\% & & T\({}_{\text{W}}\) & 5 & 75\% \\  & T\({}_{\text{F}}\) & 7 & 67.86\% & & T\({}_{\text{F}}\) & 7 & 53.57\% \\ \hline \multirow{4}{*}{\(\text{B}_{\text{M}}\)} & H & 100 & 0\% & & H & 125 & 10\% \\  & W & 50 & 25\% & & W & 60 & 16.67\% \\  & T\({}_{\text{W}}\) & 5 & 60\% & & T\({}_{\text{W}}\) & 6 & 62.5\% \\  & T\({}_{\text{F}}\) & 7 & 53.57\% & & T\({}_{\text{F}}\) & 8 & 56.25\% \\ \hline \multirow{4}{*}{\(\text{C}_{\text{C}}\)} & H & 125 & 0\% & & J\({}_{\text{S}}\) & 4 x e\({}^{5}\) & -56.25\% \\  & W & 60 & 16.67\% & & J\({}_{\text{W}}\) & 1 x e\({}^{5}\) & -275\% \\ \cline{1-1}  & T\({}_{\text{W}}\) & 6 & 58.33\% & & J\({}_{\text{SC}}\) & 3 x e\({}^{6}\) & -4.17\% \\ \cline{1-1}  & T\({}_{\text{F}}\) & 8 & 50\% & & J\({}_{\text{WC}}\) & 1 x e\({}^{6}\) & -56.25\% \\ \hline \hline \end{tabular} Note: H: Height. W: Width. T\({}_{\text{W}}\): Thickness of web. T\({}_{\text{F}}\): Thickness of flange.  
\end{table}
Table 7: Similar error of each component.  
Figure 16: Prediction error.  
calculation using ANSYS, the exact displacement of the structure is 212.6 mm. The calculation shows that the error of the structure and the completely similar result is 22.89%. Then, through the neural network obtained from the previous section, it can be seen whether the error can be accurately predicted.  
The structural parameters in this example are input into the trained neural network and the predicted target displacement is 205.75 mm. The calculation shows that the error between the predicted result and the completely similar result is 18.93% and the difference between the predicted similarity error and the calculated complete similar error is calculated, which is 3.96% within the accuracy allowed by the project. Therefore, we have obtained a set of calculation methods that can predict the structurally incomplete similarity error and the accuracy is within the controllable range.