Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161901
Title: Effect of non-Gaussian turbulence on extreme buffeting response of a high-speed railway sea-crossing bridge
Authors: Xu, Zhiwei
Dai, Gonglian
Zhang, Limao
Chen, Y. Frank
Flay, Richard G. J.
Rao, Huiming
Keywords: Engineering::Civil engineering
Issue Date: 2022
Source: Xu, Z., Dai, G., Zhang, L., Chen, Y. F., Flay, R. G. J. & Rao, H. (2022). Effect of non-Gaussian turbulence on extreme buffeting response of a high-speed railway sea-crossing bridge. Journal of Wind Engineering and Industrial Aerodynamics, 224, 104981-. https://dx.doi.org/10.1016/j.jweia.2022.104981
Journal: Journal of Wind Engineering and Industrial Aerodynamics
Abstract: The non-Gaussian property of turbulence has been emphasized in some measured wind data. However, the classic Scanlan buffeting theory with the Gaussian distribution assumption of turbulence may cause errors in predicting the structural extreme response. In this study, a hybrid approach for predicting bridge's extreme buffeting response is proposed and validated, which considers multi-dimensional non-Gaussian random variables. Then the extreme buffeting response of a cable-stayed bridge under the attack of non-Gaussian wind is investigated, where the in-situ measured wind data of Typhoon ‘Bailu’ is adopted. The sensitivity of extreme buffeting response to turbulence skewness and kurtosis is also analyzed. The study results show that the lateral and vertical buffeting displacements are in skewed distributions under the attack of Typhoon ‘Bailu’. Compared to the Gaussian wind assumption, the non-Gaussian typhoon decreases the lateral extreme response, while increases the vertical extreme response. The results from the sensitivity analysis reveal a strong correlation between the non-Gaussian intensity of component u and that of the lateral buffeting response. In addition, the lateral extreme buffeting response increases significantly with the skewness and kurtosis of component u at a high probability level.
URI: https://hdl.handle.net/10356/161901
ISSN: 0167-6105
DOI: 10.1016/j.jweia.2022.104981
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:CEE Journal Articles

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