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Wind and Structures Volume 38, Number 1, January 2024 , pages 75-91 DOI: https://doi.org/10.12989/was.2024.38.1.075 |
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Comparison of artificial intelligence models reconstructing missing wind signals in deep-cutting gorges |
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Zhen Wang, Jinsong Zhu, Ziyue Lu and Zhitian Zhang
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| Abstract | ||
| Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. NonGaussian 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. | ||
| Key Words | ||
| bridge sites; comparative study; hybrid model; signals decomposition; wind signals reconstruction | ||
| Address | ||
| Zhen Wang:School of Civil Engineering, Tianjin University, Tianjin, 300072, P.R. China Jinsong Zhu:1)School of Civil Engineering, Tianjin University, Tianjin, 300072, P.R. China 2)Key Laboratory of Coast Civil Structure Safety of Ministry of Education, School of Civil Engineering, Tianjin University, Tianjin, 300072, P.R. China Ziyue Lu:Department of Structural Engineering, Norwegian University of Science and Technology, Trondheim, 7491, Norway Zhitian Zhang:College of Civil Engineering and Architecture, Hainan University, Haikou, 570228, P.R. China | ||