Refined Process Upcrossing Rate Approach for Estimating Probabilistic Wind Load Effects with Consideration of Directionality
Publication: Journal of Structural Engineering
Volume 143, Issue 1
Abstract
The process upcrossing rate approach remains one of the most popular methods for the estimation of probabilistic wind load effect with consideration of directionality. The accuracy of this approach depends on the adequacy of the parent distribution of directional mean wind speed. The widely used Weibull distribution has been proved to be inadequate in capturing the characteristics of strong wind speed region, thus leading to unsatisfactory estimations when adopted in the process upcrossing rate approach. This study introduces a new mixed distribution model for the parent distribution of mean wind speed in order to provide a better modeling of distribution for strong wind speed region. Within this mixed model, the distribution below a prescribed wind speed threshold is given by an empirical distribution estimated from the observation data directly, whereas above the threshold, it is described in terms of the generalized Pareto distribution (GPD). The performance of the mixed distribution model is examined in estimating the yearly maximum distributions of wind speeds in each direction and regardless of direction. It is further investigated in estimating the wind load effects of buildings with consideration of directionality effect. The uncertainty of wind load effect at a given wind speed and direction is further accounted for. Numerical examples for buildings with various response characteristics demonstrate the effectiveness of the proposed framework.
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Acknowledgments
The support for this work provided in part by NSF Grant No. CMMI-1536108 is greatly acknowledged.
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Published In
Journal of Structural Engineering
Volume 143 • Issue 1 • January 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: May 29, 2015
Accepted: Jun 16, 2016
Published online: Jul 27, 2016
Discussion open until: Dec 27, 2016
Published in print: Jan 1, 2017
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