서지주요정보
Damage assessment of structural joints using structural identification techniques = 구조계 규명기법에 의한 구조물 접합부의 손상도 추정
서명 / 저자 Damage assessment of structural joints using structural identification techniques = 구조계 규명기법에 의한 구조물 접합부의 손상도 추정 / Jin-Hak Yi.
저자명 Yi, Jin-Hak ; 이진학
발행사항 [대전 : 한국과학기술원, 2001].
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등록번호

8012313

소장위치/청구기호

학술문화관(문화관) 보존서고

DCE 01007

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초록정보

This dissertation presents the development of structural identification procedure for the model updating and joint damage assessment in frame structures. It consists of three parts: i) the modal testing and identification techniques for civil engineering structures, ii) the structural joint modeling for model updating and joint damage assessment, and iii) the structural identification methods including neural networks and inverse perturbation techniques. Firstly, several modal testing methods applied to civil structures were summarized through literature survey. Particularly, the modal identification techniques without input information were introduced and applied to identify several benchmark structures. From the extensive analyses and comparisons, the mostly appropriate techniques for modal identifications are investigated considering the typical conditions of the tests. For modal parameter identification, it is highly recommended to use the frequency domain decomposition method during tests and the stochastic subspace identification method for more accurate estimation. Secondly, the structural joint modeling was presented. For the simple and realistic structural joint model, a beam element with semi-rigid connections was proposed, and the joint damage severity was defined using the reduction ratio of the joint fixity factors before and after damage. Lastly, two types of structural identification techniques were proposed for model updating and damage assessment. They are neural networks and inverse perturbation techniques. Several techniques were also utilized to improve the identification performance: such as substructural identification, noise injection learning for the neural networks, and statistical approach using measurement data perturbation scheme. The proposed techniques were verified through a numerical simulation study for a 2-bay 10-story building and an experimental study for a 2-bay and 4-story frame structure. It was found that the inverse perturbation scheme is more efficient for baseline updating, while the neural networks technique is more effective for damage identification. It was also found that the noise injection learning could significantly reduce the effects of measurement noise and the combined usage of modal and static data can improve the identification performance. Through verification by experimental results, the substructural identification was found to be very feasible for damage assessment of large and complex structural systems.

서지기타정보

서지기타정보
청구기호 {DCE 01007
형태사항 ix, 155 p. : 삽도 ; 26 cm
언어 영어
일반주기 저자명의 한글표기 : 이진학
지도교수의 영문표기 : Chung-Bang Yun
지도교수의 한글표기 : 윤정방
학위논문 학위논문(박사) - 한국과학기술원 : 토목공학과,
서지주기 Reference : p. 109-127
주제 Structural Identification
Joint Damage Assessment
Neural Networks Technique
Inverse Perturbation Technique
Modal Parameter Identification
구조계 규명기법
접합부 손상추정
신경망기법
역섭동법
모드계수추정
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