A data-driven artificial neural network model for predicting wind load of buildings using GSM-CFD solver

Wind load prediction is critical to the design of buildings, especially skyscrapers, because wind-induced dynamic loads and vibrations can often be a major concern that dominates the design. This paper presents a real-time wind load predictor for buildings by training an artificial neural network, with big data generated from a well-established software package for computational fluid dynamics (CFD), known as GSM-CFD. The cross-section of a building is idealized as a rectangle with various aspect ratios. The attack angles and velocity of the wind are also treated as variables in a wide range for building design considerations. We firstly establish a large number of computational models of major possible situations, and intensive computations using the GSM-CFD solver are conducted to generate detailed flow fields. Due to the high Reynold numbers of the flow fields, large eddy simulation (LES) model has been adopted in the calculation process. Both drag and lift coefficients are then computed based on the pressure distribution around the surface of the rectangular blocks. The GSM-CFD enables us to generate a mass of data considering different aspect ratios, wind velocities and attacking angles. Finally, an artificial neural network is trained to predict the drag coefficients for any given aspect ratios of the rectangular cross-sections of building subject to any given attack angles of wind with various velocities. The trained neural network is verified by comparing the results of the neural network predictions and that of the numerical simulations. The accuracy based on the test data set is found about 3.17%, which is sufficient for engineering design purposes. Our trained neural network model is applicable to other problems with a flow around rectangles with aspect ratios from 0.2 to 1, air-flow velocity of 34 mks, and attack angles from 0 degrees to 90 degrees. The prediction is practically in real-time. (C) 2021 Elsevier Masson SAS. All rights reserved.