Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/33256
Title: Derivation of time-varying mean for non-stationary downburst winds
Authors: Su, Y
Huang, G
Xu, YL 
Keywords: Discrete wavelet transform
Downburst wind
Empirical mode decomposition
Kernel regression
Non-stationary
Time-varying mean
Issue Date: 2015
Publisher: Elsevier
Source: Journal of wind engineering and industrial aerodynamics, 2015, v. 141, p. 39-48 How to cite?
Journal: Journal of wind engineering and industrial aerodynamics 
Abstract: Non-stationary extreme winds cause significant damages to buildings and other structures worldwide. Accurate modeling of these winds is crucial to the evaluation of structural safety. However, the derivation of a reasonable time-varying mean for these extreme winds appears to be not straightforward due to the non-stationarity, which is different from the stationary boundary layer winds. Currently, a variety of techniques have been developed to derive the time-varying mean for non-stationary winds, such as moving average, kernel regression (KR), discrete wavelet transform (DWT) and empirical mode decomposition (EMD). However, these approaches with different parameters may lead to inconsistent time-varying means and ensuing fluctuations. The evaluation of these approaches and corresponding non-stationary wind effects on structures has not been sufficiently addressed in previous research. In this study, two sets of full-scale non-stationary downburst wind records are used as examples to evaluate the performance of three approaches including KR, DWT and ensemble EMD with different time window sizes in deriving the time-varying mean. Based on these evaluations, the recommendations about the selection of the appropriate approach and time window size to derive a reasonable time-varying mean are provided.
URI: http://hdl.handle.net/10397/33256
ISSN: 0167-6105
EISSN: 1872-8197
DOI: 10.1016/j.jweia.2015.02.008
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