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 predict wind coefficients of rectangular buildings, polynomial and nonlinear regression were studied. Also, artificial neural networks (ANNs) were used as well to train, simulate and forecast wind coefficients using terrain, side ratio (D/B) and aspect ratio (H/B) as inputs. The neural networks used include BP (Back Propagation), RBF (Radial Basis Function) and GR (General Regression) neural networks. According to the investigation presented in this paper, RBF neural network is the most effective mean to predict wind coefficients.
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