Journal of Korean Association for Spatial Structures (한국공간구조학회논문집)

Korean Association for Spatial Structures (한국공간구조학회)
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Damage Detection in Truss Structures Using Deep Learning Techniques

딥러닝 기술을 이용한 트러스 구조물의 손상 탐지

  • Lee, Seunghye (Dept. of Architectural Engineering, Sejong University) ;
  • Lee, Kihak (Dept. of Architectural Engineering, Sejong University) ;
  • Lee, Jaehong (Dept. of Architectural Engineering, Sejong University)
  • Received : 2018.11.20
  • Accepted : 2018.12.05
  • Published : 2019.03.15
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Abstract

There has been considerable recent interest in deep learning techniques for structural analysis and design. However, despite newer algorithms and more precise methods have been developed in the field of computer science, the recent effective deep learning techniques have not been applied to the damage detection topics. In this study, we have explored the structural damage detection method of truss structures using the state-of-the-art deep learning techniques. The deep neural networks are used to train knowledge of the patterns in the response of the undamaged and the damaged structures. A 31-bar planar truss are considered to show the capabilities of the deep learning techniques for identifying the single or multiple-structural damage. The frequency responses and the elasticity moduli of individual elements are used as input and output datasets, respectively. In all considered cases, the neural network can assess damage conditions with very good accuracy.

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References

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