1 research outputs found
Investigation of wind pressures on tall building under interference effects using machine learning techniques
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% of a dataset to predict both mean and fluctuating pressure coefficients on
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% to 90%. It
was found that the GANs model based on 30% of the dataset is capable of
predicting both mean and fluctuating pressure coefficients under unseen
interference conditions accurately. By using this GANs model, 70% of the wind
tunnel test cases can be saved, largely alleviating the cost of this kind of
wind tunnel testing study.Comment: 15 pages, 14 figure