# On Wind-Induced Fatigue of Curtain Wall Supporting Structure of a High-Rise Building
## 4 Conclusions  
This study mainly explores wind-induced fatigue of curtain wall supporting structures, providing a reference case for the wind-induced fatigue analysis of similar structures. The research process and conclusion are listed:  
1. The wind tunnel test and finite element analysis of the curtain wall model show that the maximum wind pressure of the wall partition of Block A is 1.89 kPa, which occurs in the northwest area. The maximum equivalent stress value obtained by static analysis of the curtain wall is 26.173 Mpa, and the connection between structure and aluminum alloy frame is applied with larger stress.
2. Introduce a joint distribution model to fit local wind velocity and direction. The results indicate that the probability density function of extreme value type II is in good  
Figure 10: Node-azimuth-wind velocity-probability life in northwest region.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**S/N** & **Azimuth (\({}^{\circ}\))** & **Wind Velocity (m/s)** & **Probability** & **Life (10\({}^{\textbf{N}}\))** & **Probability Life (10\({}^{\textbf{N}}\))** \\ \hline
1 & 75 & 11.1 & \(3.69\times 10^{-8}\) & 10.45 & 3.01 \\ \hline
2 & 240 & 10.1 & \(2.17\times 10^{-5}\) & 7.76 & 3.10 \\ \hline
3 & 225 & 10.1 & \(2.07\times 10^{-5}\) & 7.81 & 3.13 \\ \hline
4 & 300 & 7.1 & \(5.89\times 10^{-5}\) & 7.42 & 3.19 \\ \hline
5 & 360 & 12.1 & \(1.47\times 10^{-4}\) & 7.04 & 3.21 \\ \hline
6 & 330 & 9.1 & \(1.90\times 10^{-4}\) & 6.96 & 3.24 \\ \hline
7 & 30 & 9.1 & \(1.03\times 10^{-5}\) & 8.28 & 3.30 \\ \hline
8 & 315 & 8.1 & \(1.77\times 10^{-4}\) & 7.12 & 3.36 \\ \hline
9 & 195 & 9.1 & \(2.30\times 10^{-5}\) & 8.00 & 3.37 \\ \hline
10 & 15 & 10.1 & \(3.64\times 10^{-7}\) & 9.88 & 3.44 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Some working conditions with shorter probability life of node 4 in northwest area.  
agreement with the local wind velocity and direction distribution. Corresponding parameters are calculated in accordance with the wind velocity probability density model and azimuth probability density model, to form a joint probability density function of wind velocity and direction.
* Data are regressed by the radial basis neural network regression algorithm, and the regression deviation is tested. According to findings, fatigue life data obey the normal distribution, generalized extreme value distribution, and kernel distribution; azimuth angles of 50\({}^{\circ}\)\(\sim\)100\({}^{\circ}\), 120\({}^{\circ}\)\(\sim\)200\({}^{\circ}\), and 260\({}^{\circ}\)\(\sim\)300\({}^{\circ}\) are found in working conditions with low probability life, and this part accounts for the largest proportion of all probability life conditions. About 95% of fatigue damage is found in the first 30 working conditions, with fatigue damage value between 3.5 \(\times\) 10\({}^{-3}\)\(\sim\)9.36 \(\times\) 10\({}^{-2}\).  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, visualization, H.L.; supervision, project administration, funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
The work described in this paper is fully supported by the National Natural Science Foundation of China Project [Grant number: 51678233] and [Grant number: 52178476].  
Not applicable.  
The authors declare no conflict of interest.