Smart Structures and Systems

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Automated structural modal analysis method using long short-term memory network

  • Jaehyung Park (Department of Civil Engineering, Chungbuk National University) ;
  • Jongwon Jung (Department of Civil Engineering, Chungbuk National University) ;
  • Seunghee Park (School of Civil, Architectural Engineering & Landscape Architecture, Sungkyunkwan University) ;
  • Hyungchul Yoon (Department of Civil Engineering, Chungbuk National University)
  • Received : 2021.12.28
  • Accepted : 2022.08.30
  • Published : 2023.01.25
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Abstract

Vibration-based structural health monitoring is used to ensure the safety of structures by installing sensors in structures. The peak picking method, one of the applications of vibration-based structural health monitoring, is a method that analyze the dynamic characteristics of a structure using the peaks of the frequency response function. However, the results may vary depending on the person predicting the peak point; further, the method does not predict the exact peak point in the presence of noise. To overcome the limitations of the existing peak picking methods, this study proposes a new method to automate the modal analysis process by utilizing long short-term memory, a type of recurrent neural network. The method proposed in this study uses the time series data of the frequency response function directly as the input of the LSTM network. In addition, the proposed method improved the accuracy by using the phase as well as amplitude information of the frequency response function. Simulation experiments and lab-scale model experiments are performed to verify the performance of the LSTM network developed in this study. The result reported a modal assurance criterion of 0.8107, and it is expected that the dynamic characteristics of a civil structure can be predicted with high accuracy using data without experts.

Keywords

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2021R1A4A3033128 and NRF-2022R1C1C1003012).

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