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Predicting roof-surface wind pressure induced by conical vortex using a BP neural network combined with POD

Building Simulation volume 15, pages 1475–1490 (2022)Cite this article

Abstract

This study aims to examine the feasibility of predicting surface wind pressure induced by conical vortex using a backpropagation neural network (BPNN) combined with proper orthogonal decomposition (POD), in which a 1:150 scaled model with a large-span retractable roof was tested in wind tunnel under both laminar and turbulent flow conditions. The distributions of mean and fluctuating wind pressure coefficients were first described, and the effects of inflow turbulence, wind direction, roof opening were examined separately. For the prediction of wind pressure, the POD-BPNN model was trained using measurement data from adjacent points. The prediction results are overall satisfactory. The root-mean-square-error (RMSE) between test and predicted data lies mostly within 10%. In particular, the prediction of mean wind pressure is found to be better than that of fluctuating wind pressure. The outcomes in this study highlight that the proposed POD-BPNN model can be well used as a useful tool to predict surface wind pressure.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their insightful and valuable comments. This project was funded by grants from the National Natural Science Foundation of China (No. 51778072 and No. 51408062) and Practice Innovation and Entrepreneurship Enhancement Plan of CSUST (SJCX202021).

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Authors and Affiliations

  1. School of Civil Engineering, Changsha University of Science and Technology, Changsha, 410082, China

    Fubin Chen & Wen Kang

  2. School of Civil Engineering, Central South University, Changsha, 410083, China

    Zhenru Shu

  3. Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong, China

    Qiusheng Li & Kang Zhou

  4. Hunan Provincial Key Laboratory of Structures for Wind Resistance and Vibration Control & School of Civil Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China

    Yi Li

  5. Department of Civil Engineering, The Pennsylvania State University, Middletown, PA, 17057, USA

    Y. Frank Chen

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  1. Fubin Chen
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  2. Wen Kang
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  3. Zhenru Shu
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  4. Qiusheng Li
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Correspondence to Zhenru Shu or Kang Zhou.

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Chen, F., Kang, W., Shu, Z. et al. Predicting roof-surface wind pressure induced by conical vortex using a BP neural network combined with POD. Build. Simul. 15, 1475–1490 (2022). https://doi.org/10.1007/s12273-021-0867-7

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  • DOI: https://doi.org/10.1007/s12273-021-0867-7

Keywords

  • wind tunnel test
  • roof-surface wind pressure
  • conical vortex
  • proper orthogonal decomposition (POD)
  • backpropagation neural network (BPNN)