A NOVEL GAN-BASED METHOD FOR BUILDING SURFACE WIND PRESSURE PREDICTION

The wind pressure on the surface of buildings can be critical, especially for high-rise buildings. To overcome the limitations in applying time-consuming CFD simulations to early design stage, we introduce a novel approach for generating real-time surface wind pressure in high-rise buildings through Deep Learning (DL). The DL model is trained on datasets of hundreds of buildings geometries generated by Grasshopper plugin EvoMass, and their corresponding surface wind pressure is simulated by RhinoCFD. Moreover, regarding the current performance prediction using DL, we have noted that the common labeling map and DL model may hinder the efficiency of training. Thus, we proposed a new labeling approach and a modified DL model to assess the accuracy of the prediction. To demonstrate the validity of the approach, the experiments of simulation predictions were conducted, and the results shows that the presented method can greatly enhance the accuracy.