Structural Displacement Prediction of Power Transmission Tower using Finite-Element Modelling and Deep Learning
Authors: 
Baosheng Li, Jishui Han, Yuan Cheng, Chong Tan, Peng Qi, Jianping Zhang, Xiaolei LiAuthors Info & Claims
Baosheng Li, Jishui Han, Yuan Cheng, Chong Tan, Peng Qi, Jianping Zhang, Xiaolei LiAuthors Info & ClaimsAbstract
The operation safety of power transmission infrastructure takes an important role in power industrial applications. This paper investigates the problem of structural displacement predication of a power transmission tower for online monitoring and early warning. First, finite-element modelling (FEM) is addressed to establish the digital model of a studied power transmission tower. Additionally, key monitoring positions of the tower are determined by the FEM under vibration analysis. At last, a recurrent neural network is proposed to forecast the displacements of the key monitoring positions ahead of time steps. The experimental results demonstrate the implementation and performance of the proposed method for displacement prediction of a power transmission tower.
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- Structural Displacement Prediction of Power Transmission Tower using Finite-Element Modelling and Deep Learning
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Published In
November 2023
237 pages
ISBN:9798400709272
DOI:10.1145/3638682
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 22 May 2024
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VSIP 2023
VSIP 2023: 2023 the 5th International Conference on Video, Signal and Image Processing
November 24 - 26, 2023
Harbin, China
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