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RISS 인기검색어
- 검색키워드
- 저자 : Jann N. Yang (검색결과 4 건)
- 무료
- 기관 내 무료
- 유료
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Protective systems for high-technology facilities against microvibration and earthquake
Yang, Jann N.,Agrawal, Anil K. Techno-Press 2000 Structural Engineering and Mechanics, An Int'l Jou Vol.10 No.6
Microvibration of high technology facilities, such as semiconductor plants and facilities with high precision equipments, due to nearby road and rail traffic has attracted considerable attention recently. In this paper, a preliminary study is conducted for the possible use of various protective systems and their performance for the reduction of microvibration. Simulation results indicate that passive base isolation systems, hybrid base isolation systems, passive floor isolation systems, and hybrid floor isolation systems are quite effective and practical. In particular, the performances of hybrid floor isolation systems are remarkable. Further, passive energy dissipation systems are not effective for the reduction of microvibration. Finally, the protections against both microvibration and earthquake are also investigated and presented.
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Comparison of various structural damage tracking techniques based on experimental data
Huang, Hongwei,Yang, Jann N.,Zhou, Li Techno-Press 2010 Smart Structures and Systems, An International Jou Vol.6 No.9
An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.
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Comparison of various structural damage tracking techniques based on experimental data
Hongwei Huang,Jann N. Yang,Li Zhou 국제구조공학회 2010 Smart Structures and Systems, An International Jou Vol.6 No.9
An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.
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Damage identification of substructure for local health monitoring
Huang, Hongwei,Yang, Jann N. Techno-Press 2008 Smart Structures and Systems, An International Jou Vol.4 No.6
A challenging problem in structural damage detection based on vibration data is the requirement of a large number of sensors and the numerical difficulty in obtaining reasonably accurate results when the system is large. To address this issue, the substructure identification approach may be used. Due to practical limitations, the response data are not available at all degrees of freedom of the structure and the external excitations may not be measured (or available). In this paper, an adaptive damage tracking technique, referred to as the sequential nonlinear least-square estimation with unknown inputs and unknown outputs (SNLSE-UI-UO) and the sub-structure approach are used to identify damages at critical locations (hot spots) of the complex structure. In our approach, only a limited number of response data are needed and the external excitations may not be measured, thus significantly reducing the number of sensors required and the corresponding computational efforts. The accuracy of the proposed approach is illustrated using a long-span truss with finite-element formulation and an 8-story nonlinear base-isolated building. Simulation results demonstrate that the proposed approach is capable of tracking the local structural damages without the global information of the entire structure, and it is suitable for local structural health monitoring.
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