Extreme estimation of wind pressure with unimodal and bimodal probability density function characteristics: A maximum entropy model based on fractional moments

The complicated probability density function (PDF) characteristics of wind pressure, including its highly skewed, highly leptokurtic, and bimodal characteristics induced by sophisticated architecture, call for improved extremeestimation models. Most existing methods are based on a unimodal PDF's underlying assumption and fail in highly non-Gaussian cases. Owing to the complete probabilistic information used in cumulative distribution function (CDF)-mapping methods, such methods have the potential to deal with complicated PDF-type wind pressures. To yield better curve-fitting of the parent distribution, which is the critical step of CDF-mapping, a maximum entropy model based on fractional moments is combined with CDF-mapping in this work. By using the Legendre-Gauss quadrature rule, the computational efficiency of fitting parent distribution is improved. The model's performance is benchmarked against typical long-term wind pressure data obtained from wind tunnel tests conducted at a long-span airport terminal model. By considering the captured probability properties, four typical taps are selected to provide empirical wind-pressure extreme in the 57% fractile for detailed model assessment. The confidence intervals of the estimated results and errors for all taps are also calculated. Compared with existing polynomial-fitting- and CDF-mapping-based translation methods, the extreme estimated by the proposed method are shown to be more robust and stable.