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 m/s, and attack angles from 0° to 90°. The prediction is practically in real-time.

风载荷预测对于建筑物（尤其是摩天大楼）的设计至关重要，因为风引起的动态载荷和振动通常是控制设计的主要问题。本文通过训练一个人工神经网络，提供了一个实时的建筑物风荷载预测器，其中的大数据来自一个完善的计算流体动力学（CFD）软件包GSM-CFD。建筑物的横截面理想化为具有各种纵横比的矩形。出于建筑设计的考虑，风的迎角和风速也被视为大范围的变量。首先，我们建立了大量可能出现的主要情况的计算模型，然后使用GSM-CFD求解器进行大量计算以生成详细的流场。由于流场的雷诺数较高，因此在计算过程中采用了大涡模拟（LES）模型。然后，基于矩形块表面周围的压力分布来计算阻力系数和升力系数。GSM-CFD使我们能够生成考虑不同纵横比，风速和迎角的大量数据。最终，训练了一个人工神经网络，以预测建筑物矩形横截面的任何给定纵横比与各种速度的风的任意给定迎角之间的阻力系数。通过比较神经网络预测结果和数值模拟结果，可以验证训练后的神经网络。根据测试数据集得出的准确度约为3.17％，这足以用于工程设计目的。我们训练有素的神经网络模型适用于其他问题，这些问题围绕纵横比​​为0.2到1的矩形流动，气流速度为34 m / s，攻角为0°到90°。该预测实际上是实时的。