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Multivariate Extreme Wind Loads: Copula-Based Analysis
A probabilistic approach is developed to estimate multivariate extreme wind loads by introducing copula theory. Three major concerns are addressed: (1) the characterization of pairwise dependence among extreme load coefficients, (2) the construction of a probability model of multivariate extreme load coefficients, and (3) the probabilistic estimation of multivariate extreme wind loads with randomness of the mean wind speed (i.e., wind climate change). Theoretical and numerical analyses are carried out with the aid of wind tunnel data. The results show that using rank dependence (Kendall’s tau and Spearman’s rho) is more appropriate than using Pearson correlation coefficient in defining dependence for extreme load coefficients. The Gaussian copula is convenient for deriving the joint distribution of multivariate extreme load coefficients but is not applicable for high-dimensional problems. In contrast, the vine copula is flexible and can provide a better estimate of the joint distribution function without dimension limitations. Multivariate annual maximum wind loads can be estimated via either first- or full-order methods. Dependence of the extreme load coefficients and randomness of the wind speed are both found having effects on the dependence of extreme wind loads. Moreover, the procedure of simulating multivariate annual maximum wind loads is presented to facilitate the use in practical problems.
Multivariate Extreme Wind Loads: Copula-Based Analysis
A probabilistic approach is developed to estimate multivariate extreme wind loads by introducing copula theory. Three major concerns are addressed: (1) the characterization of pairwise dependence among extreme load coefficients, (2) the construction of a probability model of multivariate extreme load coefficients, and (3) the probabilistic estimation of multivariate extreme wind loads with randomness of the mean wind speed (i.e., wind climate change). Theoretical and numerical analyses are carried out with the aid of wind tunnel data. The results show that using rank dependence (Kendall’s tau and Spearman’s rho) is more appropriate than using Pearson correlation coefficient in defining dependence for extreme load coefficients. The Gaussian copula is convenient for deriving the joint distribution of multivariate extreme load coefficients but is not applicable for high-dimensional problems. In contrast, the vine copula is flexible and can provide a better estimate of the joint distribution function without dimension limitations. Multivariate annual maximum wind loads can be estimated via either first- or full-order methods. Dependence of the extreme load coefficients and randomness of the wind speed are both found having effects on the dependence of extreme wind loads. Moreover, the procedure of simulating multivariate annual maximum wind loads is presented to facilitate the use in practical problems.
Multivariate Extreme Wind Loads: Copula-Based Analysis
J. Eng. Mech.
Ji, Xiaowen (author)
2023-01-01
Article (Journal)
Electronic Resource
English
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