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|Title:||Comparative analysis between different optimization-based frameworks for optimal sensor placement purposes||Authors:||Dahr, Rena||Advisors:||Nasr, Dana||Subjects:||Civil engineering
University of Balamand--Dissertations
Due 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.
Includes bibliographical references (p. 65-70).
|URI:||https://scholarhub.balamand.edu.lb/handle/uob/3977||Rights:||This 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 holder||Type:||Thesis|
|Appears in Collections:||UOB Theses and Projects|
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