Wind and Structures

Techno-Press (테크노프레스)
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Predicting the lateral displacement of tall buildings using an LSTM-based deep learning approach

  • Bubryur Kim (Department of Robot and Smart System Engineering, Kyungpook National University) ;
  • K.R. Sri Preethaa (Department of Computer Science and Engineering, KPR Institute of Engineering and Technology) ;
  • Zengshun Chen (School of Civil Engineering, Chongqing University) ;
  • Yuvaraj Natarajan (Department of Robot and Smart System Engineering, Kyungpook National University) ;
  • Gitanjali Wadhwa (Department of Computer Science and Engineering, KPR Institute of Engineering and Technology) ;
  • Hong Min Lee (Engineering Co., Ltd.)
  • Received : 2022.08.08
  • Accepted : 2023.01.12
  • Published : 2023.06.25
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Abstract

Structural health monitoring is used to ensure the well-being of civil structures by detecting damage and estimating deterioration. Wind flow applies external loads to high-rise buildings, with the horizontal force component of the wind causing structural displacements in high-rise buildings. This study proposes a deep learning-based predictive model for measuring lateral displacement response in high-rise buildings. The proposed long short-term memory model functions as a sequence generator to generate displacements on building floors depending on the displacement statistics collected on the top floor. The model was trained with wind-induced displacement data for the top floor of a high-rise building as input. The outcomes demonstrate that the model can forecast wind-induced displacement on the remaining floors of a building. Further, displacement was predicted for each floor of the high-rise buildings at wind flow angles of 0° and 45°. The proposed model accurately predicted a high-rise building model's story drift and lateral displacement. The outcomes of this proposed work are anticipated to serve as a guide for assessing the overall lateral displacement of high-rise buildings.

Keywords

Acknowledgement

This research was supported by Kyungpook National University Research Fund, 2021.

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