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 Steel and Composite Structures, An International Journal Steel and Composite Structures, An International Journal Vol.29 No.6
- 발행연도
- 2018.1
- 수록면
- 703 - 716 (14page)
이용수
초록· 키워드
오류제보하기
Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, Nystrőm method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.
목차
등록된 정보가 없습니다.
참고문헌 (49)
참고문헌 신청
Alonso, L. / 2018 / Middleware and communication technologies for structural health monitoring of critical infrastructures: A survey / Comput. Stand. Interf. 56(Supplement C):83~100
An, D. / 2015 / Practical options for selecting data-driven or physics-based prognostics algorithms with reviews / Reliab. Eng. Syst. Saf 133:223~236
Aronszajn, N. / 1950 / Theory of reproducing kernels / Trans. Am. Math. Soc 68(3):337~404
Berlinet, A. / 2011 / Reproducing kernel Hilbert spaces in probability and statistics / Springer Science & Business Media
Boller, C. / 2009 / Encyclopedia of structural health monitoring / John Wiley & Sons
An, D. / 2015 / Practical options for selecting data-driven or physics-based prognostics algorithms with reviews / Reliab. Eng. Syst. Saf 133:223~236
Aronszajn, N. / 1950 / Theory of reproducing kernels / Trans. Am. Math. Soc 68(3):337~404
Berlinet, A. / 2011 / Reproducing kernel Hilbert spaces in probability and statistics / Springer Science & Business Media
Boller, C. / 2009 / Encyclopedia of structural health monitoring / John Wiley & Sons
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