Due to their complex geometry, it is challenging to assess wind effects on the freeform, double-curved building facades. The traditional building code EN 1991-1-4 (730035) only accounts for basic shapes such as cubes, spheres, and cylinders. Moreover, even though wind tunnel measurements are considered to be more precise than other methods, they are still limited by the number of measurement points that can be taken. This limitation, combined with the time and resources required for the analysis, can limit the ability to fully capture detailed wind effects on the whole complex freeform shape of the building. In this study, we propose the use of neural network models trained to predict wind pressure on complex double-curved facades. The neural network is a powerful data- driven machine learning technique that can, in theory, learn an approximation of any function from data, making it well-suited for this application. Our approach was empirically evaluated using a set of 31 points measured in the wind tunnel on a 3D printed model in 1:300 scale of the real architectural design of a concert hall in Ostrava. The results of this evaluation demonstrate the effectiveness of our neural network method in estimating wind pressures on complex freeform facades.
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