A deep learning framework for wind pressure super-resolution reconstruction
Xiao Chen,Xinhui Dong,Pengfei Lin,Fei Ding,Bubryur Kim,Jie Song,Yiqing Xiao,Gang Hu
Abstract
Strong wind is the main factors of wind-damage of high-rise buildings, which often creates largely economical
losses and casualties. Wind pressure plays a critical role in wind effects on buildings. To obtain the high-resolution wind pressure
field, it often requires massive pressure taps. In this study, two traditional methods, including bilinear and bicubic interpolation,
and two deep learning techniques including Residual Networks (ResNet) and Generative Adversarial Networks (GANs), are
employed to reconstruct wind pressure filed from limited pressure taps on the surface of an ideal building from TPU database. It
was found that the GANs model exhibits the best performance in reconstructing the wind pressure field. Meanwhile, it was
confirmed that k-means clustering based retained pressure taps as model input can significantly improve the reconstruction
ability of GANs model. Finally, the generalization ability of k-means clustering based GANs model in reconstructing wind
pressure field is verified by an actual engineering structure. Importantly, the k-means clustering based GANs model can achieve
satisfactory reconstruction in wind pressure field under the inputs processing by k-means clustering, even the 20% of pressure
taps. Therefore, it is expected to save a huge number of pressure taps under the field reconstruction and achieve timely and
accurately reconstruction of wind pressure field under k-means clustering based GANs model.
Key Words
buildings; deep learning; generative adversarial networks; super resolution; wind pressure
Address
Xiao Chen, Xinhui Dong and Pengfei Lin — Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China
Fei Ding — NatHaz Modeling Laboratory, University of Notre Dame, Notre Dame, IN 46556, USA
Bubryur Kim — Department of Robot and Smart System Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Korea
Jie Song — Research Center of Urban Disasters Prevention and Fire Rescue Technology of Hubei Province, School of Civil Engineering, Wuhan University, Wuhan, China
Yiqing Xiao and Gang Hu — 1)Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China 2)Guangdong Provincial Key Laboratory of Intelligent and Resilient Structures for Civil Engineering, Harbin Institute of Technology, Shenzhen, 518055, China 3)Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications, Harbin Institute of Technology, Shenzhen, 518055, China
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