Wind and Structures

Techno-Press (테크노프레스)
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Prediction of downburst-induced wind pressure coefficients on high-rise building surfaces using BP neural network

  • Fang, Zhiyuan (School of Civil Engineering, Chongqing University) ;
  • Wang, Zhisong (School of Civil Engineering, Chongqing University) ;
  • Li, Zhengliang (School of Civil Engineering, Chongqing University)
  • Received : 2019.06.18
  • Accepted : 2019.12.10
  • Published : 2020.03.25
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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.

Keywords

Acknowledgement

Supported by : Central Universities, National Natural Science Foundation of China

The research reported in this paper was conducted with the support of the National Key R&D Program of China (Grant no. 2018YFC0809400) the Fundamental Research Funds for Central Universities (Grant no. CDJZR12200016) and National Natural Science Foundation of China (Grant no. 51208537). The authors are grateful to Dr. Yong Chen in Zhejiang University for his kindly helps and good suggestions during the Impinging jet experiment.

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