Influence of dependence of directional extreme wind speeds on wind load effects with various mean recurrence intervals

•Improved understanding of the influence of dependence between directional wind speeds is offered.•Several factors that influence the prediction with and without consideration of dependence are discussed by using Gaussian copula model.•The difference brought by different joint probability distribution models is discussed through a comparison between multivariate Gaussian and Gumbel copula models.•The necessity of using multivariate approach is discussed and a simplified method is proposed to account for directional dependence.•Discussion is made on the partition of directional sectors which concerns the balance of number of sectors and modeling uncertainty.

This study offers an improved understanding of the influence of statistical dependence between directional extreme wind speeds when estimating the wind load effects with various mean recurrence intervals (MRIs). Existing multivariate approaches that concern the wind directionality effect are first reviewed. Several factors that influence the prediction with and without consideration of the statistical dependence between directional extreme wind speeds are discussed by using Gaussian copula model. The influence of wind speed masking on the wind effect estimation is discussed. The influence of use of different joint probability distribution models for directional extreme wind speeds is illustrated through a comparison between multivariate Gaussian and Gumbel copula models. The necessity of using multivariate approach is discussed and a simplified method is proposed to account for directional dependence, which not only provides accurate prediction but also reduces calculation effort. Examples with real wind climate model and generic wind tunnel test results are shown to illustrate the influences brought by directional dependence, model difference, and wind speed masking. Also discussion is made on the partition of directional sectors which concerns the balance of number of sectors and modeling uncertainty.