Abstract Interference effects of tall buildings have attracted numerous studies due to the boom of clusters of buildings in megacities. To fully understand the interference effects, 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. Four machine learning models including decision tree, random forest, XGBoost, generative adversarial networks (GANs), were trained based on 30% of a dataset to predict wind 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 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.

中文翻译：

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基于深度学习的干扰效应下高层建筑风压研究

摘要 由于特大城市建筑群的蓬勃发展，高层建筑的干扰效应引起了大量研究。要充分了解干扰效应，通常需要进行大量的风洞测试。仅覆盖部分干扰场景的有限风洞测试无法完全揭示干扰影响。本研究使用机器学习技术来解决产生不可靠结果的有限风洞测试与成本高昂的干扰效应的完整调查之间的冲突要求。四种机器学习模型包括决策树、随机森林、XGBoost、生成对抗网络 (GAN)，基于 30% 的数据集进行训练，以预测主要建筑物的风压系数。GAN 模型在预测这些压力系数方面表现出最佳性能。然后根据数据集的不同部分训练了许多 GAN 模型，范围从 10% 到 90%。结果表明，基于 30% 数据集的 GANs 模型能够准确预测未知干扰条件下的压力系数。通过使用这种 GANs 模型，可以节省 70% 的风洞测试用例，大大减轻了这种风洞测试研究的成本。