## 3 Results and Discussion
### Distribution Characteristic of the Fluctuating Pressure  
In both wind tunnel experiments and field measurements, numerous instantaneous negative pulses, referred to as pressure peaks [31; 32], are observed in the fluctuating wind pressures. These pressure signals from the tests can be utilized to assess the non-Gaussian nature of the fluctuating pressure by examining the PDF results. Figure 11 illustrates the PDF at three key pressure taps on the test model in the 0\({}^{\circ}\) direction: tap 3 on the windward side, tap 6 on the lateral side, and tap 13 on the leeward side, respectively. It is evident that the PDF results for the windward side point align with a Gaussian distribution, whereas the results for the lateral and leeward points exhibit non-Gaussian characteristics. Additionally, the non-Gaussian characteristics at tap 6 on the lateral side are notably more pronounced than those observed at tap 13 on the leeward side, indicating a higher presence of wind shear and vorticity on the lateral side [33]. Moreover, when comparing the PDF outcomes from different wind tunnel facilities, the discrepancies appear to be relatively insignificant. As previously highlighted, the primary impact of different wind tunnel systems on turbulent wind flow lies in the turbulence integral scale. Therefore, it can be inferred that the effect on the PDF of pressure fluctuation is minimal. [PERSON] et al. in 2022 [11] studied the impact of turbulence integral scale on the non-Gaussian characteristics of surface pressure and extreme wind loads on the CAARC model. Similar phenomena were also observed in their study. Figure 10: Percentage errors before and after correction: (**a**) in the 0\({}^{\circ}\) direction; and (**b**) in the 90\({}^{\circ}\) direction. Figure 9: Corrected average wind pressure coefficient: (**a**) in the 0\({}^{\circ}\) direction; and (**b**) in the 90\({}^{\circ}\) direction. The extreme wind loads for engineering design are usually calculated as follows:  
\[g=\frac{C_{\text{pi}}-C_{\text{p,mean}}}{C_{\text{p,rms}}} \tag{7}\]  
\[C_{\text{p,extreme}}=C_{\text{p,mean}}\pm gC_{\text{p,rms}} \tag{8}\]  
where \(C_{\text{p,extreme}}\) is the extreme value of the wind pressure coefficient and g is the peak factor. Obviously, the value of g is the key parameter for calculating the accurate extreme wind loads. In current design standards such as GB50009 (National Standard of the People's Republic of China, 2012), ASCE 7-16 (ASCE, 2016), and AS/NZS 1170.2 (AS/NZS, 2011) [24; 34; 35], the peak factor is conventionally calculated under the classical Gaussian process assumption. However, as demonstrated by the aforementioned findings, significant non-Gaussian characteristics are observed in most areas of the structure surface, particularly in the separation zone. Consequently, wind loads are often underestimated, leading to frequent instances of structural damage and even failures in tall buildings subjected to strong winds [36; 37; 38]. For the Chinese code GB50009, a peak factor of 2.5 is employed with a non-exceedance probability of 99.38%, assuming a classical Gaussian process. To contrast this with the Chinese code's peak factor, a non-Gaussian peak factor is calculated with the same non-exceedance probability, corresponding to a Cumulative Distribution Function (CDF) value of 0.9938. Figure 12 illustrates that the peak factor estimated at tap 6 on the lateral side at a wind angle of 0\({}^{\circ}\) is 3.2. In this analysis, the three-parameter Weibull distribution is chosen to represent the PDF, aligning well with the non-Gaussian nature of fluctuating pressure. Figure 11: PDF at 0\({}^{\circ}\) wind angle: (**a**) tap 3 on the windward side; (**b**) tap 6 on the lateral side; (**c**) tap 13 on the leeward side. Figure 12: CDF of measurement point 6 at 0\({}^{\circ}\) wind direction angle.