Computation of pressure envelope on setback buildings using artificial intelligence due to wind load

Face-wise pressure variation on regular square-shaped (SQ) and two different types of setback high-rise building modes have been focused on in this study. Two building models are considered as single-side single setback (SB1) and single-side double setback (SB2). This study showcased the pressure contour envelope, which presented the maximum to minimum range of pressure variation for the 0°–180° range of attacking wind angles. According to this, the designers and researchers can use it to estimate pressure variation for any attacking wind angle. Computational fluid dynamics (CFD) and artificial neural networks (ANNs) have been employed to analyze the data sets. The 70% training data considered from CFD data can efficiently plot the pressure contour envelop. The SB2 model efficiently tries to downplay a 12%–14% average velocity. The outcome of this study indicates that the SB2 model skillfully reduces the wind speed over the building, as well as the setback distance and setback number. • The designers and researchers can easily predict the pressure coefficient range on a particular face of the setback building without analysis of individual wind angles. • A single contour can represent the pressure coefficient for any wind incidence angles. • Artificial intelligence is used to predict the pressure contour, which minimizes the effort and cost of a wind tunnel study. • The single-sided double setback building model minimizes the aerodynamic effects on different faces. • Setback distance and setback number reduce the wind speed over the building.