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.28 No.3
- 발행연도
- 2021.9
- 수록면
- 375 - 390 (16page)
이용수
초록· 키워드
오류제보하기
Structural health monitoring (SHM) is currently applied to control regularly the health of high-rise buildings which have deteriorated after being subjected to a sudden loading. Damage detection at element levels of a structure consisting of an enormous number of elements becomes the main objective. In this study, the complicated problem is simplified by a two-step solution. Damaged storeys are preliminarily detected before a full damage scenario at an element level is achieved. In Step 1, to overcome the issues related to the huge number of degrees of freedom (DOFs), the full building is simplified to a beam-like system using the Guyan condensation technique. As the natural characteristics of the two lowest modes at the intact and a damaged stage are obtained, the eigenvalue problem based inverse solution is applied to approximately detect damaged storeys. Furthermore, an updating procedure that is proposed in this study effectively enhances the first prediction. In Step 2, an artificial neural network (ANN) model is designed to indicate damaged members on detected storeys using only the first three modal modes. Compared to other approaches applied to detect damages on high-rise buildings, the robustness of the proposed method is that the required number of lowest modal modes is two and three in Step 1 and Step 2 respectively. Furthermore, regardless of the extension of the building in the horizontal direction, only one lateral displacement of each storey is measured to detect damaged storeys in Step 1 and generally detect damaged elements in Step 2. For light and asymmetrical damage scenarios, two more vertical displacements should be considered to obtain accurate element-level detection. However, for all cases, the required number of DOFs is significantly lower than the full system.
목차
등록된 정보가 없습니다.
참고문헌 (38)
참고문헌 신청
1985 / ACI Committee Report SP-97, Response of Buildings to Lateral Forces / American Concrete Institute
Bakhary, N. / 2008 / Structural condition monitoring and damage identification with artificial neural network
Beale, M. H. / 2018 / Neural Network Toolbox™ Getting Started Guide / The MathWorks Inc
M.N. CERRI / 2000 / DETECTION OF DAMAGE IN BEAMS SUBJECTED TO DIFFUSED CRACKING / Journal of Sound and Vibration 234(2):259~276
Chia-Ming Chang / 2018 / Applications of neural network models for structural health monitoring based on derived modal properties / Measurement 129:457~470
Bakhary, N. / 2008 / Structural condition monitoring and damage identification with artificial neural network
Beale, M. H. / 2018 / Neural Network Toolbox™ Getting Started Guide / The MathWorks Inc
M.N. CERRI / 2000 / DETECTION OF DAMAGE IN BEAMS SUBJECTED TO DIFFUSED CRACKING / Journal of Sound and Vibration 234(2):259~276
Chia-Ming Chang / 2018 / Applications of neural network models for structural health monitoring based on derived modal properties / Measurement 129:457~470
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