Prediction of mean and RMS wind pressure coefficients for low-rise buildings using deep neural networks

• Published in: Engineering structures 2023-01, Vol.274, p.115149, Article 115149
• Main Authors: Huang, Youqin, Ou, Guanheng, Fu, Jiyang, Zhang, Honghao
• Format: Article
• Language: English
• Subjects: Accuracy; Aerodynamic database; Artificial neural networks; Correlation coefficient; Correlation coefficients; Deep learning; Deep neural networks; Error analysis; Flow separation; General regression neural networks; Low rise buildings; Machine learning; Mathematical models; Nested model; Neural networks; Predictions; Pressure; Pressure gradients; Roofing; Roofs; Wind; Wind pressure; Wind pressure coefficients
• ISSN: 0141-0296; 1873-7323
• DOI: 10.1016/j.engstruct.2022.115149

•Deep neural network is employed for wind pressure prediction for the first time.•Errors of all taps on roof are exhaustively analysed by multiple error metrics.•The accuracy in roof ridge and corner bay is evidently higher than the literature.•A nested DNNs model is proposed to improve the accuracy in the corner zone. Although the problems of wind pressure prediction on roofs have been studied extensively, the prediction accuracy is still unsatisfactory owing to the limited capacity of shallow learning of artificial neural networks (ANNs), especially in the areas characterized by flow separation. Moreover, the literatures have only conducted error analysis on part of the predicted taps instead of all taps, and have not made precision comparison with related studies. In this paper, the model of deep neural networks (DNNs) with the ability of deep learning are built to predict the mean and root-mean-square (RMS) wind pressure coefficients on low-rise buildings. For quantitatively comparing the presented results with those from the literature, the same buildings from the NIST-UWO database as studied by the literature are also predicted in this work. In order to improve the DNNs model for predicting the corner zone with higher pressure gradients, a nested DNNs model is further proposed by returning the mean coefficients predicted by one DNNs model as the input of another DNNs model for the RMS coefficients. The prediction results of the DNNs model are also compared with those from the methods of random forest (RF) and general regression neural network (GRNN). The study shows that the DNNs model obviously enhances the prediction accuracy around the roof ridge and the corner bay in comparison with the ANNs model. For the mean or RMS coefficients of all the taps on the whole roof, the correlation coefficient between predicted and experimental results exceeds 0.997, the mean-square-error (MSE) is less than 5 %, and the relative errors of>95 % predicted samples are lower than 10 %. The prediction accuracy in the corner zone is further improved by the nested network where the samples within errors less than 5 % are evidently increased and the errors > 20 % disappear. Also, the prediction results from the DNNs model are apparently better than those from RF and GRNN. This study could provide a benchmark for future studies on the prediction of mean or RMS wind pressure coefficients on the roofs.