Investigation of wind pressures on tall building under interference effects using machine learning techniques
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Interference effects of tall buildings have attracted numerous studies due to the boom of clusters of tall buildings in megacities. To fully understand the interference effects of buildings, it often requires a substantial amount of wind tunnel tests. Limited wind tunnel tests that only cover part of interference scenarios are unable to fully reveal the interference effects. This study used machine learning techniques to resolve the conflicting requirement between limited wind tunnel tests that produce unreliable results and a completed investigation of the interference effects that is costly and time-consuming. Four machine learning models including decision tree, random forest, XGBoost, generative adversarial networks (GANs), were trained based on 30 the principal building. The GANs model exhibited the best performance in predicting these pressure coefficients. A number of GANs models were then trained based on different portions of the dataset ranging from 10 was found that the GANs model based on 30 predicting both mean and fluctuating pressure coefficients under unseen interference conditions accurately. By using this GANs model, 70 tunnel test cases can be saved, largely alleviating the cost of this kind of wind tunnel testing study.
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