Techno Press
Techno Press

Smart Structures and Systems
  Volume 28, Number 3, September 2021 , pages 391-405
DOI: https://doi.org/10.12989/sss.2021.28.3.391
 

Predicting wind-induced structural response with LSTM in transmission tower-line system
Jiayue Xue and Ge Ou

 
Abstract
    Wind-induced dynamic response of the nonlinear structure is critical for the structural safety and reliability. The traditional approaches for this response including observation or simulation focus on the structural health monitoring, the experiment, or finite element model development. However, all these approaches require high cost or computational investment. This paper proposes to predict the wind-induced dynamic response of the nonlinear structure with a novel deep learning approach, LSTM, and applies this in a structural lifeline system, the transmission tower-line system. By constructing the optimized LSTM architectures, the proposed method applies to both the linear structure, the single transmission tower and the nonlinear structure, the transmission tower-line system, with promising results for the dynamic and extreme response prediction. It can conclude that the layers and the hidden units have a strong impact on the LSTM prediction performance, and with proper training data set, the computational time can significantly decrease. A comparison surrogate model developed by CNN is also utilized to demonstrate the robustness of the LSTM-based surrogate model with limited data scale.
 
Key Words
    dynamic response; nonlinear structure; LSTM; RNN; wind engineering
 
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Department of Civil and Environmental Engineering, University of Utah, 110 Central Campus Dr., Ste. 2000, Salt Lake City, UT, United States.
 
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