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This paper presents a generalized approach for predicting (i.e., interpolating) the magnitude and distribution of roof pressures near separated flow regions on a low-rise structure based on freestream turbulent flow conditions. A feed-forward multilayer artificial neural network (ANN) using a backpropagation (BP) training algorithm is employed to predict the mean, root-mean-square (RMS), and peak pressure coefficients on three geometrically scaled (1:50, 1:30, and 1:20) low-rise building models for a family of upwind approach flow conditions. A comprehensive dataset of recently published boundary layer wind tunnel (BLWT) pressure measurements was utilized for training, validation, and evaluation of the ANN model. On average, predicted ANN peak pressure coefficients for a group of pressure taps located near the roof corner were within 5.1, 6.9, and 7.7% of BLWT observations for the 1:50, 1:30, and 1:20 models, respectively. Further, very good agreement was found between predicted ANN mean and RMS pressure coefficients and BLWT data.

Abstract: The pressures on the roof of a 6m cube in open country are considered. These show significant similarities between the instantaneous pressure distribution during an extreme event and the mean distributions. High local suctions are observed during oblique flows but for larger areas the design load may occur during more orthogonal flows since the flow is more highly correlated. It is shown that while quasi-steady methods may fail to predict local short duration suction peaks, the approach is effective in predicting the pressures on even moderately small areas of the order of 3% of the total roof area. [Copyright &y& Elsevier]