Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/3977
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dc.contributor.advisorNasr, Danaen_US
dc.contributor.authorDahr, Renaen_US
dc.date.accessioned2020-12-23T14:39:34Z-
dc.date.available2020-12-23T14:39:34Z-
dc.date.issued2019-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/3977-
dc.descriptionIncludes bibliographical references (p. 65-70).en_US
dc.descriptionSupervised by Dr. Dana Nasr.en_US
dc.description.abstractDue to the development in the field of monitoring technology, a considerable amount of unnecessary measurement data becomes abundantly available. Optimal sensor placement methods are consequently used to provide the user with the most informative observed data that requires the least time and cost to be analyzed for damage detection purposes. In this thesis, a robust optimal sensor placement approach that comprises combinations of an optimization-based algorithm, the Simulated Annealing (SA), with the Ensemble Kalman Filter technique (EnKF), was presented for Structural Health Monitoring purposes. SA algorithm generates randomly an initial population of sensor locations. The objective function for the SA was determined by minimizing the difference between the actual measured displacements and velocities of the different floors of the building and their corresponding predicted values calculated using the EnKF technique. The ones with the minimum difference between real and predicted data represent the best sensor combinations for damage detection and system identification purposes. A comparative analysis between different optimization frameworks, primarily the GA-EnKF and SA-EnKF, was performed. Both algorithms were tested on the same building subjected to the same excitation exerted at its base. The comparison is based on the computational burden and the accuracy of the results of each methodology in converging to the best sensor locations. The validity and accuracy of the GA-EnKF and the SA-EnKF results were evaluated and compared to the optimal sensor locations of the Brute-Force search methodology. The percentage of convergence of the SA-EnKF approach to the optimal results of the Brute-Force method is lower than that of the GA-EnKF framework. However, the computational burden of the SA-EnKF method is lower than that of the GA-EnKF approach.en_US
dc.description.statementofresponsibilityby Rena Dahren_US
dc.format.extentx, 70 p. :ill., tables ;30 cm +1 CD-Rom (4 3/4 in.)en_US
dc.language.isoengen_US
dc.rightsThis object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holderen_US
dc.titleComparative analysis between different optimization-based frameworks for optimal sensor placement purposesen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Civil Engineeringen_US
dc.contributor.facultyFaculty of Engineeringen_US
dc.contributor.institutionUniversity of Balamanden_US
dc.date.catalogued2020-01-07-
dc.description.degreeMS in Civil Engineeringen_US
dc.description.statusUnpublisheden_US
dc.identifier.OlibID247606-
dc.rights.accessrightsThis item is under embargo until end of year 2021.en_US
dc.provenance.recordsourceOliben_US
Appears in Collections:UOB Theses and Projects
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