Wind and Structures

  • 제36권6호
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  • Pages.367-377
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  • 2023
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  • 1226-6116(pISSN)
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  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

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Extrapolation of wind pressure for low-rise buildings at different scales using few-shot learning

  • Yanmo Weng (Zachry Department of Civil and Environmental Engineering, Texas A&M University) ;
  • Stephanie G. Paal (Zachry Department of Civil and Environmental Engineering, Texas A&M University)
  • 투고 : 2022.07.31
  • 심사 : 2023.01.12
  • 발행 : 2023.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.

키워드

과제정보

The authors gratefully acknowledge the support from Dr. Chowdhury's research group at Florida International University. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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