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 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.27 No.6
- 발행연도
- 2021.1
- 수록면
- 903 - 916 (14page)
이용수
초록· 키워드
오류제보하기
This study presents a damage detection method based on modal responses for building structures using convolutional neural networks (CNNs). The modal responses used in the method are obtained from the dynamic responses, which are measured in a building structure under ambient excitations; these are then transformed to a modal participation ratio (MPR) value for a measuring point and mode. As modal responses vary after damages in the structures, the MPR for a specific location and mode also changes. Thus, in this study, MPR variations, which can be obtained by comparing the MPRs of damaged and healthy structures, are utilized for damage detection without the need for identification of modal parameters. Since MPRs are derived for the number of measuring points (<i>N</i>) in the structure as well as the same number of modes (<i>N</i>), the MPRs and MPR variations can be arranged as an <i>N</i> × <i>N</i> matrix. This low-dimensional MPR variations set is used as the input map of the presented CNN architecture and information about damage locations and severities of the target structure is set as the output of the CNN. The presented CNN is trained for establishing the relationship between MPR variations and damage information and utilized to estimate the damage. The presented damage detection method is applied to numerical examples for two multiple degrees of freedoms and a three-dimensional ASCE benchmark numerical model. Training datasets created from damage scenarios assuming changes in the stiffness are used to train the CNN and the performance of this CNN is verified. Finally, this study examines how variations in the operator size and number of layers in the CNN architecture affect the damage detection performance of CNNs.
목차
등록된 정보가 없습니다.
참고문헌 (44)
참고문헌 신청
Osama Abdeljaber / 2017 / Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks / Journal of Sound and Vibration 388:154~170
Osama Abdeljaber / 2018 / 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data / Neurocomputing 275:1308~1317
Fu-Chen Chen / 2018 / NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion / IEEE Transactions on Industrial Electronics 65(5):4392~4400
Jun-Da Chen / 2018 / Two-stage damage detection algorithms of structure using modal parameters identified from recursive subspace identification / Earthquake Engineering & Structural Dynamics 47(3):573~593
Congcong Chen / 2018 / Hyperspectral classification based on spectral–spatial convolutional neural networks / Engineering Applications of Artificial Intelligence 68:165~171
Osama Abdeljaber / 2018 / 1-D CNNs for structural damage detection: Verification on a structural health monitoring benchmark data / Neurocomputing 275:1308~1317
Fu-Chen Chen / 2018 / NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion / IEEE Transactions on Industrial Electronics 65(5):4392~4400
Jun-Da Chen / 2018 / Two-stage damage detection algorithms of structure using modal parameters identified from recursive subspace identification / Earthquake Engineering & Structural Dynamics 47(3):573~593
Congcong Chen / 2018 / Hyperspectral classification based on spectral–spatial convolutional neural networks / Engineering Applications of Artificial Intelligence 68:165~171
함께 읽어보면 좋을 논문
논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!
-
Hierarchical neural network for damage detection using modal parameters
Structural Engineering and Mechanics, An Int＇l Journal
2019 .01
-
Vibration-based method for story-level damage detection of the reinforced concrete structure
Computers and Concrete, An International Journal
2021 .01
-
Damage detection in plate structures using frequency response function and 2D-PCA
Smart structures and systems
2017 .01
-
Damage detection in plate structures using frequency response function and 2D-PCA
Smart Structures and Systems, An International Journal
2017 .01
-
Damage detection on a full-scale highway sign structure with a distributed wireless sensor network
Smart structures and systems
2015 .01
-
Damage detection using both energy and displacement damage index on the ASCE benchmark problem
Structural Engineering and Mechanics, An Int＇l Journal
2021 .01
-
An improved modal strain energy method for structural damage detection, 2D simulation
Structural engineering and mechanics : An international journal
2015 .01
-
석탑문화재 손상 유형 및 영향 요인에 따른 손상도 평가
한국공간구조학회지
2018 .01
-
Damage assessment of shear-type structures under varying mass effects
Structural monitoring and maintenance
2019 .01
-
층강성 손상비를 이용한 전단형 건물의 손상위치 추정에 관한 연구
대한건축학회 논문집 - 구조계
2018 .02
-
Damage detection of plate-like structures using intelligent surrogate model
Smart Structures and Systems, An International Journal
2016 .01
-
An improved modal strain energy method for structural damage detection, 2D simulation
Structural Engineering and Mechanics, An Int＇l Journal
2015 .01
-
Damage detection of plate-like structures using intelligent surrogate model
Smart structures and systems
2016 .01
-
딥러닝 기술을 이용한 트러스 구조물의 손상 탐지
한국공간구조학회지
2019 .01
-
Structural damage identification for elements and connections using an improved genetic algorithm
Smart Structures and Systems, An International Journal
2021 .11
-
모델기반의 계측데이터 확장 및 손상 추정에 관한 연구
대한건축학회 논문집 - 구조계
2018 .03
-
Numerical evaluation for vibration-based damage detection in wind turbine tower structure
Wind & structures
2015 .01
-
Refinement of damage identification capability of neural network techniques in application to a suspension bridge
Structural monitoring and maintenance
2015 .01
-
Class activation map을 활용한 변위/변형률 정보 기반 구조물 손상 탐지 기법
풍력에너지저널
2021 .09
이 논문의 저자 정보
최근 본 자료
댓글(0)
0