Formulation of Estimation Models for Wind Force Coefficients of Rectangular Shaped Buildings

Jenmu  Wang; Chii-Ming  Cheng

doi:10.6180/jase.2017.20.1.07

Formulation of Estimation Models for Wind Force Coefficients of Rectangular Shaped Buildings

Jenmu Wang This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and Chii-Ming Cheng¹

¹Department of Civil Engineering, Tamkang University, Tamsui, Taiwan 251, R.O.C.

Received: April 20, 2016
Accepted: October 14, 2016
Publication Date: March 1, 2017

Download Citation: ||https://doi.org/10.6180/jase.2017.20.1.07

ABSTRACT

In wind-resistant design of structures, the calculation of wind coefficients is usually based on data from wind tunnel tests. The process is very time-consuming and expensive. In order to formulate a model to estimate wind force coefficients of rectangular buildings, various methods including regression analysis and artificial neural networks (ANNs) were investigated. This paper focuses on the presentation of the various approaches with emphasis on the detailed result comparisons and discussions of models developed for alongwind, acrosswind and tortional wind coefficient predictions.

Keywords: Wind Force Coefficients, Regression, Artificial Neural Networks, Aerodynamic Database

REFERENCES

[1] Wang, J. and Cheng, C. M., “The Application of Artificial Neural Networks to Predict Wind Spectra for Rectangular Cross-Section Buildings,” Proceedings of Fifth International Symposium on Computational Wind Engineering (CWE2010), Chapel Hill, North Carolina, U.S.A, May 2327 (2010). doi: 10.5359/jawe. 35.347
[2] Wang, J. and Cheng, C. M., “The Role of Artificial Neural Networks in a Building Design Wind Load Expert System Based on Aerodynamic Databases,” ICWE 13, Jul. 1015, Amsterdam, Netherlands, Paper #191 (2011).
[3] Wang, J. and Cheng, C. M., “Web-Enabled Design Wind Load Expert System for Tall Buildings,” Proceedings of the 6th Asia-Pacific Conference on Wind Engineering (APCWE VI), Seoul, Korea, Sep. 12~14, pp. 329339 (2005).
[4] Cheng, C. M., Lin, Y. Y., Wang, J., Wu, J. C. and Chang, C. H., “The Aerodynamic Database for the Interference Effects of Adjacent Tall Buildings,” Conference Preprints, 12th International Conference on Wind Engineering, Cairns, Australia, Jul. 16, Vol. 1, pp. 359366 (2007).
[5] Cheng, C. M., Lin, Y. Y., Wang, J., Wu, J. C. and Chang, C. H., “The Aerodynamic Database for the Interference Effects of Adjacent Tall Buildings,” Conference Preprints, 12th International Conference on Wind Engineering, July 16, Cairns, Australia, Vol. 1, pp. 359366 (2007).
[6] Cheng, C. M., Wang, J. and Chang, C. H., “e-wind: an Integrated Engineering Solution Package for Wind Sensitive Buildings and Structures,” Journal of Wind & Engineering, Vol. 5, No. 2, pp. 5059 (2008).
[7] Bitsuamlak, G. T. and Godbole, P. N., “Application of Cascade-correlation Learning Network Fordetermination of Wind Pressure Distribution in Buildings,” Wind Engineering into the 21st Century, Balkema, Rotterdam (1999).
[8] Chen, Y., Kopp, G. A. and Surry, D., “Prediction of Pressure Coefficients on Roofs of Low Buildings Using Artificial Neural Networks,” Journal of Wind Engineering and Industrial Aerodynamics, Vol. 91, pp. 423441 (2003). doi: 10.1016/S0167-6105(02)00 381-1
[9] Krian, J., Gašparac, G., Kozmar, H., Antoni, O. and Grisogono, B., “Designing Laboratory Wind Simulations Using Artificialneural Networks,” Theoretical and Applied Climatology, Vol. 120, No. 3, pp. 723 736 (2015). doi: 10.1007/s00704-014-1201-4
[10] Wang, J. and Cheng, C. M., “Aero-Data Based Wind Resistant Design of Rectangular Shaped Tall Buildings,” International Conference on Innovations in Civil and Structural Engineering (ICICSE’15), June 34, Istanbul, Turkey (2015).