Prediction of pressure coefficients on roofs of low buildings using artificial neural networks

This paper describes an artificial neural network (ANN) approach for the prediction of mean and root-mean-square (rms) pressure coefficients on the gable roofs of low buildings. The ANN models, which employ a backpropagation training algorithm, are capable of generalizing the complex, nonlinear functional relationships between the pressure coefficients and eave height, wind direction and spatial location on the roof. The performance of the ANN is demonstrated by the prediction of the pressure coefficients for roof tap locations in a corner bay. The mean bay uplift can be predicted accurately with an average error less than 2% for three cornering wind directions not seen by the ANN during training. The mean-square errors of all of the individual pressure taps in the corner bay were 12% and 9% for the mean and rms coefficients, respectively. This approach could be used to expand aerodynamic databases to a larger variety of geometries and increase its practical feasibility. (C) 2002 Elsevier Science Ltd. All rights reserved.