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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.23 No.5
- 발행연도
- 2019.1
- 수록면
- 507 - 520 (14page)
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초록· 키워드
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In the structural health monitoring field, damage detection has been commonly carried out based on the structural model and the engineering features related to the model. However, the extracted features are often subjected to various errors, which makes the pattern recognition for damage detection still challenging. In this study, an automated damage identification method is presented for hanger cables in a tied-arch bridge using a convolutional neural network (CNN). Raw measurement data for Fourier amplitude spectra (FAS) of acceleration responses are used without a complex data pre-processing for modal identification. A CNN is a kind of deep neural network that typically consists of convolution, pooling, and fully-connected layers. A numerical simulation study was performed for multiple damage detection in the hangers using ambient wind vibration data on the bridge deck. The results show that the current CNN using FAS data performs better under various damage states than the CNN using time-history data and the traditional neural network using FAS. Robustness of the present CNN has been proven under various observational noise levels and wind speeds.
목차
등록된 정보가 없습니다.
참고문헌 (56)
참고문헌 신청
Zhang, R. / 2018 / Temperature compensation of Elasto-Magneto-Electric(EME)sensors in cable force monitoring using BP neural network / Sensors 18(7):2176
Miikkulainen, R. / 2017 / Evolving Deep Neural Networks / arXiv: Neural and Evolutionary Computing :293~312
Jiyoung Min / 2012 / Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity / Engineering Structures 39:210~220
Chathurdara Sri Nadith Pathirage / 2018 / Structural damage identification based on autoencoder neural networks and deep learning / Engineering Structures 172:13~28
Scherer, D. / 2010 / Evaluation of pooling operations in convolutional architectures for object recognition / Paper presented at the international conference on artificial neural networks
Miikkulainen, R. / 2017 / Evolving Deep Neural Networks / arXiv: Neural and Evolutionary Computing :293~312
Jiyoung Min / 2012 / Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity / Engineering Structures 39:210~220
Chathurdara Sri Nadith Pathirage / 2018 / Structural damage identification based on autoencoder neural networks and deep learning / Engineering Structures 172:13~28
Scherer, D. / 2010 / Evaluation of pooling operations in convolutional architectures for object recognition / Paper presented at the international conference on artificial neural networks
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