Abstract
The accurate characterization of the wind characteristics of extreme wind events is crucial to the structural design and safety evaluation. However, the field-measured wind data during typhoons exhibit a strong non-stationary feature with an inherent time-varying trend which deviates from the traditional stationary assumption. This paper presents the study of non-stationary wind characteristics of Typhoon Soudelor at the Jiubao Bridge located in Hangzhou, China, which has been instrumented with a structural health monitoring system. After data classification by the stationary test, the non-stationary wind data are analyzed by both traditional stationary wind model and non-stationary wind model which considers the inherent time–varying mean wind speed. The empirical mode decomposition (EMD) and wavelet multiresolution analysis (MRA) are employed to extract the underlying trend from the original wind data as the time-varying mean wind speed for the non-stationary wind model. Based on two categories of fluctuating wind speeds obtained by removing the constant and time-varying mean wind speed, the wind characteristics including the mean wind speed, turbulence intensity, gust factor, probability distribution and power spectral density are comparatively analyzed. The results indicate that the time-varying mean wind speed can be effectively identified and extracted by the EMD and MRA. The non-stationary wind model is more appropriate than the traditional stationary wind model in the analysis of non-stationary wind speed data during a typhoon.
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Acknowledgements
The work described in this paper was jointly supported by the National Science Foundation of China (Grant no. 51778574), the Fundamental Research Funds for the Central Universities of China (Grant no. 2017QNA4024), and the Key Lab of Structures Dynamic Behavior and Control (Harbin Institute of Technology), Ministry of Education of the PRC.
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Ye, X.W., Xi, P.S. & Su, Y.H. Analysis of non-stationary wind characteristics at an arch bridge using structural health monitoring data. J Civil Struct Health Monit 7, 573–587 (2017). https://doi.org/10.1007/s13349-017-0244-5
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DOI: https://doi.org/10.1007/s13349-017-0244-5