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Wind and Structures Volume 22, Number 2, February 2016 , pages 235-252 DOI: https://doi.org/10.12989/was.2016.22.2.235 |
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Wind power spectra for coastal area of East Jiangsu Province based on SHMS |
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Hao Wang, Tianyou Tao and Teng Wu
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| Abstract | ||
| A wind velocity power spectrum (WVPS) with high fidelity is extremely important for accurate prediction of structural buffeting response. WVPS heavily depends on the geographical locations, local terrains and topographies. Hence, field measurement of wind characteristics may be the unique way to obtain the accurate WVPS for a specific region. In this paper, a systematic analysis and discussions of existing WVPSs were performed. Six recorded strong wind data from the structural health monitoring systems (SHMS) of Runyang Suspension Bridge (RSB) and Sutong Cable-stayed Bridge (SCB) in Jiangsu Province of China were selected for analysis. The measured and pre-processed wind velocity data was first transformed from time domain to frequency domain to obtain the measured spectrum. The spectrum for each strong wind was then fitted using the nonlinear least square method and compared with both the fitted spectrum from statistical analysis and the recommended spectrum in specifications. The modified Kaimal spectrum was proved to be the \"best\" choice for the coastal area of East Jiangsu Province. Finally, a suitable WVPS formula fit for the coastal area of East Jiangsu Province was presented based on the modified Kaimal spectrum. Results in this study provide a more accurate and reliable WVPS for wind-resistant design of engineering structures in the coastal area of East Jiangsu Province. | ||
| Key Words | ||
| wind velocity; power spectrum; strong wind; coastal area of East Jiangsu Province; structural health monitoring | ||
| Address | ||
| Hao Wang and Tianyou Tao: Key Laboratory of C&PC Structures of Ministry of Education, Southeast University, No. 2 Sipailou,Nanjing 210096, China Teng Wu: Department of Civil, Structural and Environmental Engineering, University at Buffalo, State University of New York, Buffalo, NY 14126, USA | ||