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


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.



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.


  1. Benardos, A.G. and Kaliampakos, D.C. (2004), "A methodology for assessing geotechnical hazards for TBM tunnelling - illustrated by the Athens Metro, Greece", Int. J. Rock Mech. Min., 41(6), 987-999.
  2. Bre, F., Gimenez, J.M. and Fachinotti, V.D. (2018), "Prediction of wind pressure coefficients on building surfaces using artificial neural networks", Energy Build., 158, 1429-1441.
  3. Chay, M.T. and Letchford, C.W. (2002), "Pressure distributions on a cube in a simulated thunderstorm downburst - Part A: stationary downburts observations", J. Wind Eng. Ind. Aerod., 90(7), 711-732.
  4. Chen, C.H., Wu, J.C. and Chen, J.H. (2008), "Prediction of flutter derivatives by artificial neural networks", J. Wind Eng. Ind. Aerod., 96(10-11), 1925-1937.
  5. Chen, Y., Kopp, G.A. and Surry, D. (2003), "Prediction of pressure coefficients on roofs of low buildings using artificial neural networks", J. Wind Eng. Ind. Aerod., 91(3), 423-441.
  6. Choi, E.C.C. (2004), "Field measurement and experimental study of wind speed profile during thunderstorms", J. Wind Eng. Ind. Aerod., 92(3-4), 275-290.
  7. Damatty, A. and Huang, G. (2018), Special issue on Non-Synoptic Wind I Preface, Wind Struct., 26 (3), 1-1.
  8. Demuth, H. and Beale, M. (2009). Matlab Neural Network Toolbox User's Guide Version 6, The MathWorks Inc, Natick, Massachusetts, U.S.A.
  9. Dongmei, H., Shiqing, H., Xuhui, H., and Xue, Z. (2017), "Prediction of wind loads on high-rise building using a BP neural network combined with POD", J. Wind Eng. Ind. Aerod., 170, 1-17.
  10. English, E.C. and Fricke, F.R. (1999), "The interference index and its prediction using a neural network analysis of wind-tunnel data", J. Wind Eng. Ind. Aerod., 83(1-3), 567-575.
  11. Fu, J.Y., Liang, S.G. and Li, Q.S. (2007), "Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networks", Comput. Struct., 85(3-4), 179-192.
  12. Fujita T.T. (1985), "The Downburst", Report of Projects NIMROD and JAWS; University of Chicago, Chicago, Illinois, U.S.A
  13. Goh, A.T.C. and Zhang, W. (2012), "Reliability assessment of stability of underground rock caverns", Int. J. Rock Mech. Min., 55(10), 157-163.
  14. Hjelmfelt, M.R. (1988), "Structure and life cycle of microburst outflows observed in Colorado", J. Appl. Meteor, 27(8), 900-927.;2.
  15. Holmes, J.D. and Oliver, S.E. (2000), "An empirical model of a downburst", Eng. Struct., 22(9), 1167-1172.
  16. Huang, G., He, H., Mehta, K.C. and Liu, X. (2015), "Datab-based probabilistic damage estimation for asphalt shingle roofing", J. Struct. Eng., 141(12), 04015065.
  17. Khanduri, A.C., Bedard, C. and Stathopoulos, T. (1997), "Modelling wind-induced interference effects using backpropagation neural networks", J. Wind Eng. Ind. Aerod., 72(1), 71-79.
  18. Kim, Y.S. and Kim, B.T. (2008), "Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model", Comput. Geotech., 35(3), 313-322.
  19. Kordnaeij, A., Kalantary, F., Kordtabar, B. and Mola-Abasi, H. (2015), "Prediction of recompression index using GMDH-type neural network based on geotechnical soil properties", Soils Found., 55(6), 1335-1345.
  20. Letchford, C.W. (1999), "Turbulence and topographic effects in simulated thunderstorm downdrafts by wind tunnel jet", 10th International Conference on Wind Engineering, Copenhagen, Denmark, June.
  21. Letchford, C.W., Mans, C. and Chay, M.T. (2002), "Thunderstorms - their importance in wind engineering (a case for the next generation wind tunnel)", J. Wind Eng. Ind. Aerod., 90(12), 1415-1433.
  22. Mason, M.S., Letchford, C.W. and James, D.L. (2005), "Pulsed wall jet simulation of a stationary thunderstorm downburst, Part A: Physical structure and flow field characterization", J. Wind Eng. Ind. Aerod., 93(7), 557-580.
  23. Nejad, F.P. and Jaksa, M.B. (2017), "Load-settlement behavior modeling of single piles using artificial neural networks and CPT data", Comput. Geotech., 89, 9-21.
  24. Peng, L., Huang, G., Chen, X. and Yang, Q. (2018), "Evolutionary spectra-based time-varying coherence function and application in structural response analysis to downburst winds", J. Struct. Eng., 144(7):04018078.
  25. Rumelhart, D. (1988), Learning Internal Representation by Error Propagation, MIT Press, Cambridge, Massachusetts, U.S.A.
  26. Wood, G.S., Kwok, K.C.S., Motteram, N.A. and Fletcher, D.F. (2001), "Physical and numerical modelling of thunderstorm downbursts", J. Wind Eng. Ind. Aerod., 89(6), 535-552.
  27. Yang, Q., Gao, R., Bai, F., Li, T. and Tamura, Y. (2018), "Damage to buildings and structures due to recent devastating wind hazards in East Asia", Nat. Hazards, 92(3), 1-33.
  28. Zhang, W. and Goh, A.T.C. (2016), "Multivariate adaptive regression splines and neural network models for prediction of pile drivability", Geosci. Front, 7(1), 45-52.
  29. Zhang, W.G. and Goh, A.T.C. (2013), "Multivariate adaptive regression splines for analysis of geotechnical engineering systems", Comput. Geotech., 48, 82-95.

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