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Wind and Structures Volume 30, Number 3, March 2020 , pages 289-298 DOI: https://doi.org/10.12989/was.2020.30.3.289 |
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Prediction of downburst-induced wind pressure coefficients on high-rise building surfaces using BP neural network |
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Zhiyuan Fang, Zhisong Wang and Zhengliang Li
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| Abstract | ||
| Gusts generated by downburst have caused a great variety of structural damages in many regions around the world. It is of great significance to accurately evaluate the downburst-induced wind load on high-rise building for the wind resistance design. The main objective of this paper is to propose a computational modeling approach which can satisfactorily predict the mean and fluctuating wind pressure coefficients induced by downburst on high-rise building surfaces. In this study, using an impinging jet to simulate downburst-like wind, and simultaneous pressure measurements are obtained on a high-rise building model at different radial locations. The model test data are used as the database for developing back propagation neural network (BPNN) models. Comparisons between the BPNN prediction results and those from impinging jet test demonstrate that the BPNN-based method can satisfactorily and efficiently predict the downburst-induced wind pressure coefficients on single and overall surfaces of high-rise building at various radial locations. | ||
| Key Words | ||
| downburst; pressure coefficient; high-rise building; impinging jet; BP neural network | ||
| Address | ||
| Zhiyuan Fang: School of Civil Engineering, Chongqing University, Chongqing 400045, China Zhisong Wang: School of Civil Engineering, Chongqing University, Chongqing 400045, China Zhengliang Li: School of Civil Engineering, Chongqing University, Chongqing 400045, China | ||