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F. Fazelpour, Negar Tarashkar, M. Rosen (2016)
Short-term wind speed forecasting using artificial neural networks for Tehran, IranInternational Journal of Energy and Environmental Engineering, 7
V. Venkatachalapathy (2012)
Behavior of a Nuclear Power Plant Ventilation Stack for Wind LoadsJournal of The Institution of Engineers (India): Series A, 93
M. Mohandes, S. Rehman, T. Halawani (1998)
A neural networks approach for wind speed predictionRenewable Energy, 13
A. Vyavahare, P. Godbole, T. Nikose (2012)
Analysis of tall building for across wind responseInternational Journal of Civil and Structural Engineering, 2
J. Fu, S. Liang, Qiusheng Li (2007)
Prediction of wind-induced pressures on a large gymnasium roof using artificial neural networksComputers & Structures, 85
A. Zhang, Ling Zhang (2004)
RBF neural networks for the prediction of building interference effectsComputers & Structures, 82
D. Svozil, V. Kvasnicka, Jiri Pospíchal (1997)
Introduction to multi-layer feed-forward neural networksChemometrics and Intelligent Laboratory Systems, 39
B. Yan, Qiusheng Li (2016)
Wind tunnel study of interference effects between twin super-tall buildings with aerodynamic modificationsJournal of Wind Engineering and Industrial Aerodynamics, 156
A. Khanduri, C. Bédard, T. Stathopoulos (1997)
Modelling wind-induced interference effects using backpropagation neural networksJournal of Wind Engineering and Industrial Aerodynamics, 72
Yingzhao Chen, G. Kopp, D. Surry (2003)
Prediction of pressure coefficients on roofs of low buildings using artificial neural networksJournal of Wind Engineering and Industrial Aerodynamics, 91
T. Tamura, Kojiro Nozawa, K. Kondo (2006)
AIJ guide for numerical prediction of wind loads on buildingsJournal of Wind Engineering and Industrial Aerodynamics, 96
(2010)
The application of artificial neural networks to predict wind spectra for rectangular cross-section buildings
F. Bre, J. Giménez, V. Fachinotti (2018)
Prediction of wind pressure coefficients on building surfaces using artificial neural networksEnergy and Buildings, 158
Wonsul Kim, Y. Tamura, A. Yoshida (2015)
Interference effects on aerodynamic wind forces between two buildingsJournal of Wind Engineering and Industrial Aerodynamics, 147
Comparison of Cp values for 2.5B interfering distance for wind incident angle of 45°4
S. Jayalekshmi, J. Jegadesh, A. Goel (2018)
Empirical Approach for Determining Axial Strength of Circular Concrete Filled Steel Tubular ColumnsJournal of The Institution of Engineers (India): Series A, 99
X. Gavaldà, J. Ferrer-Gener, G. Kopp, F. Giralt (2011)
Interpolation of pressure coefficients for low-rise buildings of different plan dimensions and roof slopes using artificial neural networksJournal of Wind Engineering and Industrial Aerodynamics, 99
E. English, F. Fricke (1999)
The interference index and its prediction using a neural network analysis of wind-tunnel dataJournal of Wind Engineering and Industrial Aerodynamics, 83
Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations
Wind load acting on a building in presence of one or more interfering buildings usually varies when compared to isolated buildings. This effect of variations in wind loads due to presence of other interfering building is termed interference effect. This effect on wind loads is mainly governed by parameters such as geometry, orientation of the structures with respect to wind direction and upstream terrain conditions. The provisions made in design codes and standards with regard to the effect of interfering structures are inadequate for proper estimation of wind loads. As such, the structural designers opt for wind tunnel experiments to get accurate wind loading on these structures. Computational fluid dynamics (CFD) and artificial neural network (ANN) approaches are other alternative methods emerging in recent years. This paper discusses on the ANN approach to predict the pressure coefficients on building faces due to interference, which may be used further in calculating wind loads. Mean pressure coefficients on each face of principal building (building under investigation) are predicted and analyzed for 45º wind angle, in presence of an interfering building at different locations upstream of the principal building. There is a good agreement between the predicted results and those obtained using wind tunnel. The results show that ANN approach can serve as a better tool to predict wind loads at preliminary stage and make an initial estimation for different interfering cases compared to methods like CFD and experimental which are complex, time consuming and costlier.
Journal of The Institution of Engineers (India): Series A – Springer Journals
Published: Mar 29, 2021
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