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Wind and Structures Volume 36, Number 6, June 2023 (Special Issue) pages 393-404 DOI: https://doi.org/10.12989/was.2023.36.6.393 |
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Prediction of skewness and kurtosis of pressure coefficients on a low-rise building by deep learning |
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Youqin Huang, Guanheng Ou, Jiyang Fu and Huifan Wu
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| Abstract | ||
| Skewness and kurtosis are important higher-order statistics for simulating non-Gaussian wind pressure series on low-rise buildings, but their predictions are less studied in comparison with those of the low order statistics as mean and rms. The distribution gradients of skewness and kurtosis on roofs are evidently higher than those of mean and rms, which increases their prediction difficulty. The conventional artificial neural networks (ANNs) used for predicting mean and rms show unsatisfactory accuracy in predicting skewness and kurtosis owing to the limited capacity of shallow learning of ANNs. In this work, the deep neural networks (DNNs) model with the ability of deep learning is introduced to predict the skewness and kurtosis on a low-rise building. For obtaining the optimal generalization of the DNNs model, the hyper parameters are automatically determined by Bayesian Optimization (BO). Moreover, for providing a benchmark for future studies on predicting higher order statistics, the data sets for training and testing the DNNs model are extracted from the internationally open NISTUWO database, and the prediction errors of all taps are comprehensively quantified by various error metrices. The results show that the prediction accuracy in this study is apparently better than that in the literature, since the correlation coefficient between the predicted and experimental results is 0.99 and 0.75 in this paper and the literature respectively. In the untrained cornering wind direction, the distributions of skewness and kurtosis are well captured by DNNs on the whole building including the roof corner with strong non-normality, and the correlation coefficients between the predicted and experimental results are 0.99 and 0.95 for skewness and kurtosis respectively. | ||
| Key Words | ||
| aerodynamic database; deep learning; kurtosis; low-rise buildings; skewness; wind pressure coefficients | ||
| Address | ||
| Youqin Huang, Guanheng Ou, Jiyang Fu and Huifan Wu:Research Centre for Wind Engineering and Engineering Vibration, Guangzhou University, 230 West Waihuan Road, Higher Education Mega Center, Guangzhou, China : | ||