Wind Loads on Low Building Roofs: A Stochastic Perspective

Field measurements of extreme pressures on a flat roof of a low-rise building during typhoons

“…24(a) and (b) depict variations of the histograms of the third and fourth order moments of the pressure coefficients with mean approaching wind directions between 201 and 901 for Tap12 and Tap21, respectively. It is evident that there were significant deviations from the Gaussian distribution for the pressures measured at Tap12 and Tap21, as the Gaussian distribution has constant values of skewness (0.0) and kurtosis (3.0), which is consistent with the observations previously made by several researchers (e.g., Kumar and Stathopoulos, 2000;Gioffre et al, 2000) that the pressure fluctuations in separated flow regions possess non-Gaussian probability contents.…”

Comparative Study on Non‐Gaussian Characteristics of Wind Pressure for Rigid and Flexible Structures

“…In conclusion, from the probability density function, the roof of flexible structure and the surface of rigid structure have a similarity that both of them show softening non-Gaussian characteristic with kurtosis higher than 3. However, the difference between them is shown as the roof of flexible structure shows strong non-Gaussian property Shock and Vibration in the windward side, which is also found by Kumar and Stathopoulos [7], while the surface of rigid structure shows non-Gaussian property mainly in the leeward. The latter conclusion can be explained as follows: by the comprehensive effect of the separated flow and wake, the surface of rigid structure reflects negative skewness and large kurtosis (higher than 3) in the leeward side; this phenomenon has also been found by Han and Gu [15].…”

“…Figures 16(b) and 16(c) show the skewness and kurtosis of the fluctuating wind pressures on CD side and AB side which are parallel to the wind direction, and the distribution of skewness and kurtosis is similar to that in Figure 16(a). The fluctuating wind pressures of the windward (BC side) measuring points in Figure 16(d) show the strong non-Gaussian property with the kurtosis distributing mainly in the interval [3,7] and the skewness is in the interval [0.5, 2.0]. In Figure 17, it can be found that the fluctuating wind pressures of the nonwindward measuring points #1∼#5 show negative bias, while those of the windward measuring points #6∼#10 display positive or negative bias.…”

“…In some separate flow regions, the wind pressure distributions show obvious nonGaussian characteristics [4][5][6] due to the influence of air separation, reattachment, and vortex shedding. This kind of wind pressures with asymmetric distribution and sharply pulse peak, which has close correlations with the vortex movement of the wind field, leads easily to the fatigue damage of structure and is the major cause of local structural damage [7]. Compared with the Gaussian wind pressures, the non-Gaussian wind pressures take on the feature of asymmetric and/or leptokurtic distribution, which is the nonnormal distribution.…”

Statistical Modelling of Botswana’s Monthly Maximum Wind Speed using a Four-Parameter Kappa Distribution

“…For instance, it is an important parameter in the estimation of evapotranspiration for agricultural purposes (Valipour and Eslamian, 2014;Valipour, 2014a;2014b;2015a;2015b;2015c;2015d). It is also an important factor for engineers in the construction industry, who use it to calculate wind uplift loads (Liu, 1991;Dyrbye and Hansen, 1997;Rosowsky and Cheng, 1999;Kumar and Stathopoulos, 2000;Zhou et al, 2002). In this study, Botswana's wind speed is being studied mainly for renewable energy applications (Chiodo, 2013;Mukhopadhyay et al, 2014).…”