Estimation of Peak Factor of Non-Gaussian Wind Pressures by Improved Moment-Based Hermite Model

Abstract

The moment-based Hermite polynomial function model approach is often used to estimate the extreme value distribution and peak factor of a non-Gaussian process through those of the underlying Gaussian process. This paper presents a study on the performance of the moment-based model approach as applied to various non-Gaussian wind pressures on a large-span saddle-type roof by comparing the estimated peak factors with those directly derived from long-term wind-tunnel data. The results showed that the moment-based model approach can be less accurate for large amounts of non-Gaussian pressure data. One of the reasons is that the skewness and kurtosis are statistical moments affected by both positive and negative probability distribution tails, and thus are less specific in defining only one of the distribution tails, which determines the statistics of maximum or minimum. To improve the accuracy of the moment-based model approach, a new strategy is introduced that defines new statistical moments using the distribution greater or lower than the median for estimation of the distribution of maximum or minimum, respectively. Accordingly, the distributions of maximum and minimum are addressed separately using newly defined two sets of statistical moments with zero skewness. The effectiveness of the newly proposed approach is examined for various non-Gaussian wind pressures.