Wind and Structures

  • 제38권1호
  • /
  • Pages.75-91
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  • 2024
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  • 1226-6116(pISSN)
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  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Comparison of artificial intelligence models reconstructing missing wind signals in deep-cutting gorges

  • Zhen Wang (School of Civil Engineering, Tianjin University) ;
  • Jinsong Zhu (School of Civil Engineering, Tianjin University) ;
  • Ziyue Lu (Department of Structural Engineering, Norwegian University of Science and Technology) ;
  • Zhitian Zhang (College of Civil Engineering and Architecture, Hainan University)
  • 투고 : 2023.08.12
  • 심사 : 2024.01.11
  • 발행 : 2024.01.25
⟨ 이전 논문 다음 논문 ⟩

초록

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

키워드

과제정보

Authors would like to express their gratitude to the financial support from the Hainan Provincial Natural Science Foundation of China (Grant Number 520CXTD433); They are also indebted to the National Natural Science Foundation of China (Grant Number 51938012, 52268073 and 52068020).

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