Multivariate Extreme Wind Loads: Copula-Based Analysis
This article has been corrected.
VIEW CORRECTIONPublication: Journal of Engineering Mechanics
Volume 149, Issue 1
Abstract
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.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. Data item: wind tunnel data.
Acknowledgments
The support by the National Natural Science Foundation of China (Grant No. 51908014) is greatly acknowledged.
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© 2022 American Society of Civil Engineers.
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Received: Jan 21, 2022
Accepted: Aug 18, 2022
Published online: Oct 26, 2022
Published in print: Jan 1, 2023
Discussion open until: Mar 26, 2023
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