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dc.contributor.advisorJerome J. Connor.en_US
dc.contributor.authorFoun, Kevinen_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.en_US
dc.date.accessioned2010-09-01T13:41:35Z
dc.date.available2010-09-01T13:41:35Z
dc.date.copyright2009en_US
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/57987
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, February 2010.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (p. 175-179).en_US
dc.description.abstractIn the context of civil and industrial structures, structural control and damage detection have recently become an area of great interest. The safety of a structure is always the most important issue for structural engineers, and to achieve this goal, the discipline of Structural Health Monitoring (SHM) was introduced. SHM records real-time information concerning structural conditions and performances. In order to evaluate the health conditions of structures, identifying the structural parameters is needed. Research activities of this area are increasing due to the availability of computation and wireless technologies. The objective of this thesis is to evaluate the tracking ability of the Kalman filter for identifying civil structural parameters based on measured vibration data which usually are earthquake accelerations. For linear elastic structures, the ordinary Kalman filter was used, but for nonlinear elastic structures, we implemented the extended Kalman filter.en_US
dc.description.abstract(cont.) For simulating damage occurrence in structures, a sudden change of stiffness was introduced, and an adaptive extended Kalman filter was utilized to estimate the time-varying parameters. In this thesis, linear and nonlinear structures with single-degree-of-freedom and multi-degree-of-freedom were simulated. Measurements having different levels of white noise were considered in order to evaluate the effects of noise on parametric estimations. In addition, the impacts of different levels of noise covariance were also discussed. Simulation results from different structural models were presented to demonstrate the effectiveness of the Kalman filter.en_US
dc.description.statementofresponsibilityby Kevin Foun.en_US
dc.format.extent179 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectCivil and Environmental Engineering.en_US
dc.titleIdentification of civil structural parameters using the extended Kalman filteren_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Civil and Environmental Engineering.en_US
dc.identifier.oclc639574336en_US


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