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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.4
- 발행연도
- 2021.1
- 수록면
- 495 - 508 (14page)
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초록· 키워드
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This study devises a novel approach, namely quadruple 1D convolutional neural network, for detecting connection stiffness reduction in steel truss bridge structure using experimental and numerical modal data. The method is developed based on expertise in two domains: firstly, in Structural Health Monitoring, the mode shapes and its high-order derivatives, including second, third, and fourth derivatives, are accurate indicators in assessing damages. Secondly, in the Machine Learning literature, the deep convolutional neural networks are able to extract relevant features from input data, then perform classification tasks with high accuracy and reduced time complexity. The efficacy and effectiveness of the present method are supported through an extensive case study with the railway Nam O bridge. It delivers highly accurate results in assessing damage localization and damage severity for single as well as multiple damage scenarios. In addition, the robustness of this method is tested with the presence of white noise reflecting unavoidable uncertainties in signal processing and modeling in reality. The proposed approach is able to provide stable results with data corrupted by noise up to 10%.
목차
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참고문헌 (53)
참고문헌 신청
Abdeljaber, O. / 2018 / 1-D CNNs for structural damage detection : Verification on a structural health monitoring benchmark data / Neurocomput 275:1308~1317
Adeli, H. / 1988 / Expert Systems in Construction and Structural Engineering / CRC Press
American Association of State Highway and Transportation Officials / 2017 / AASHTO LRFD Bridge Design Specifications
Avci, O. / 2018 / Wireless and real-time structural damage detection : A novel decentralized method for wireless sensor networks / J. Sound Vib 424:158~172
Bakhary, N. / 2007 / Damage etection using artificial neural network with consideration of uncertainties / Eng. Struct 29(11):2806~2815
Adeli, H. / 1988 / Expert Systems in Construction and Structural Engineering / CRC Press
American Association of State Highway and Transportation Officials / 2017 / AASHTO LRFD Bridge Design Specifications
Avci, O. / 2018 / Wireless and real-time structural damage detection : A novel decentralized method for wireless sensor networks / J. Sound Vib 424:158~172
Bakhary, N. / 2007 / Damage etection using artificial neural network with consideration of uncertainties / Eng. Struct 29(11):2806~2815
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