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.

Abstract
 1. 서론
 2. 학습을 위한 신경망 구조
  2.1 대상 트러스 구조물
  2.2 동적 응답 데이터 준비
  2.3 신경망 구조
 3. 손상 탐지 단계
  3.1 훈련 단계(Training phase)
  3.2 손상 예측 단계(Prediction phase)
 4. 결과 및 분석
  4.1 단일 손상
  4.2 복수 손상
 5. 결론
 References

• 이승혜(세종대학교 건축공학과) | Seunghye Lee (Dept. of Architectural Engineering, Sejong University)
• 이기학(세종대학교 건축공학과) | Kihak Lee (Dept. of Architectural Engineering, Sejong University)
• 이재홍(세종대학교 건축공학과) | Jaehong Lee (Dept. of Architectural Engineering, Sejong University) 교신저자