지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Structural Engineering and Mechanics, An Int'l Journal Structural Engineering and Mechanics, An Int'l Journal Vol.77 No.1
- 발행연도
- 2021.1
- 수록면
- 47 - 56 (10page)
이용수
초록· 키워드
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This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.
목차
등록된 정보가 없습니다.
참고문헌 (34)
참고문헌 신청
Abdeljaber, O. / 2017 / Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks / J. Sound Vib 388:154~170
Ahmad, S. / 2019 / Multiple damage detections in plate-like structures using curvature mode shapes and gapped smoothing method / Adv. Mech. Eng 11(5):1687814019848921
Alvandi, A. / 2006 / Assessment of vibrationbased damage identification techniques / J. Sound Vib 292(1-2):179~202
Anitescu, C. / 2019 / Artificial neural network methods for the solution of second order boundary value problems / Computers, Mater. Continua 59(1):345~359
Brincker, R. / 2001 / Modal identification of output-only systems using frequency domain decomposition / Smart Mater. Struct 10(3):441
Ahmad, S. / 2019 / Multiple damage detections in plate-like structures using curvature mode shapes and gapped smoothing method / Adv. Mech. Eng 11(5):1687814019848921
Alvandi, A. / 2006 / Assessment of vibrationbased damage identification techniques / J. Sound Vib 292(1-2):179~202
Anitescu, C. / 2019 / Artificial neural network methods for the solution of second order boundary value problems / Computers, Mater. Continua 59(1):345~359
Brincker, R. / 2001 / Modal identification of output-only systems using frequency domain decomposition / Smart Mater. Struct 10(3):441
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