dc.contributor.authorGao, Fengen_US
dc.date.accessioned2008-09-25T06:41:02Z
dc.date.accessioned2017-07-23T08:26:55Z
dc.date.available2008-09-25T06:41:02Z
dc.date.available2017-07-23T08:26:55Z
dc.date.copyright2007en_US
dc.date.issued2007
dc.identifier.citationGao, F. (2007). Structural damage detection and diagnosis using time-domain data. Doctoral thesis, Nanyang Technological University, Singapore.
dc.identifier.urihttp://hdl.handle.net/10356/12234
dc.description.abstractThis study aims to develop general methodologies and implementation schemes to enhance the robustness of the time domain methods for damage diagnosis and extend the ability to the assessment of damage severity. Major contributions of this study include 1) the development of a novel time-series analysis method, which requires only acceleration response signals from the structure, for detection of the occurrence and location of damage; 2) extension of the above method to noise-contaminated vibration signals, with a novel scheme incorporating Kalman filter to establish the virtual Input-Output signal pairs that essentially represent the underlying physical system; and 3) formulation of a residual generator technique, based on geometric concept for disturbances decoupling problem (DDP), for detecting and locating the damage using acceleration measurements instead of displacements. The method also enables quantitative estimation of the severity of damage in individual elements of a complicated system. Numerical examples and experimental case studies are given to demonstrate the implementation and effectiveness of the proposed approaches.en_US
dc.format.extent176 p.
dc.language.isoen
dc.rightsNanyang Technological Universityen_US
dc.subjectDRNTU::Engineering::Civil engineering::Structures and design
dc.titleStructural damage detection and diagnosis using time-domain dataen_US
dc.typeThesisen_US
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.contributor.supervisorLu Yongen_US
dc.description.degreeDOCTOR OF PHILOSOPHY (CEE)en_US


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