Wind and Structures

  • 제36권6호
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  • Pages.405-421
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  • 2023
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  • 1226-6116(pISSN)
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  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A deep learning framework for wind pressure super-resolution reconstruction

  • Xiao Chen (Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • Xinhui Dong (Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • Pengfei Lin (Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • Fei Ding (NatHaz Modeling Laboratory, University of Notre Dame) ;
  • Bubryur Kim (Department of Robot and Smart System Engineering, Kyungpook National University) ;
  • Jie Song (Research Center of Urban Disasters Prevention and Fire Rescue Technology of Hubei Province, School of Civil Engineering, Wuhan University) ;
  • Yiqing Xiao (Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Civil and Environmental Engineering, Harbin Institute of Technology) ;
  • Gang Hu (Artificial Intelligence for Wind Engineering (AIWE) Lab, School of Civil and Environmental Engineering, Harbin Institute of Technology)
  • 투고 : 2022.08.29
  • 심사 : 2023.01.12
  • 발행 : 2023.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

This study is supported by National Key R&D Program of China (2021YFC3100702), National Natural Science Foundation of China (52108451), Shenzhen Science and Technology Program (SGDX20210823103202018), Shenzhen Science and Technology Innovation Commission (GXWD20201230155427003-20200823230021001), Shenzhen Science and Technology Program (KQTD20210811090112003), and Guangdong-Hong KongMacao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications (2020B1212030001). The authors highly appreciate the aerodynamic database of Tokyo Polytechnic University.

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