Revisiting moment-based characterization for wind pressures

• Davenport’s formula (DF) and moment-based Hermite polynomial model (HPM) are evaluated via very long wind pressure data.
• HPM should be used in the peak value estimation if the skewness and kurtosis are sufficient to capture its non-Gaussianity.
• DF causes large errors in peak factor estimation for nearly Gaussian wind pressures while HPM provides a robust estimation.

The estimation of peak wind pressures is important in the reliability- and performance-based design for low-rise buildings. Typically, Davenport׳s formula is widely used to determine the peak factor if the pressure approximately follows Gaussian distribution. Recently, the moment-based Hermite polynomial model (HPM) is becoming popular to estimate the peak factor when the non-Gaussianity of wind pressure exists. However, their performances deserve further study based on the appropriate wind tunnel data. In this study, Davenport׳s formula and moment-based HPM are reviewed. The peak value of wind pressure is determined using very long time histories of wind pressure data to evaluate the performance of moment-based HPM and Davenport׳s formula. Results suggest that moment-based HPM should be adopted in the peak value estimation for wind pressures when the skewness and kurtosis of a process are sufficient to capture its non-Gaussian properties. Results also show that Davenport׳s formula may cause noticeable errors in the peak factor estimation for the wind pressure data close to Gaussian process while HPM provides a robust estimation for these data.