Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/6087
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dc.contributor.authorNasr, Danaen_US
dc.contributor.authorDahr, Reina Elen_US
dc.contributor.authorAssaad, Josephen_US
dc.contributor.authorKhatib, Jamalen_US
dc.date.accessioned2022-10-11T07:06:48Z-
dc.date.available2022-10-11T07:06:48Z-
dc.date.issued2022-09-05-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/6087-
dc.description.abstractThe arbitrary placement of sensors in concrete structures measures a considerable amount of unnecessary data. Optimal sensor placement methods are used to provide informative data with the least cost and maximum efficiency. In this study, a robust optimal sensor placement framework that combines an optimization-based algorithm, the simulated annealing (SA) algorithm, and the ensemble Kalman filter (EnKF) are presented for structural health monitoring and system identification. The SA algorithm randomly generates an initial population of sensor locations, while the framework undergoes a minimization process. The objective function used is the difference between the actual measured data and their corresponding EnKF predicted values. A comparative analysis between the genetic algorithm–ensemble Kalman filter (GA-EnKF) and the simulated annealing–ensemble Kalman filter (SA-EnKF) approaches is presented. The performance and computational burden of both algorithms, which converge to the best sensor locations for damage detection purposes, are tested on a 10-story building subjected to a seismic excitation. The results are compared to the optimal sensor locations of the brute-force search methodology. The GA-EnKF outperforms the SA-EnKF in terms of accuracy in converging to the optimal results, yet the computational cost of the SA-EnKF is considerably lower.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.subjectDamage detectionen_US
dc.subjectEnsemble Kalman filteren_US
dc.subjectGenetic algorithmen_US
dc.subjectOptimal sensor placementen_US
dc.subjectSimulated annealingen_US
dc.subjectStructural health monitoringen_US
dc.subjectSystem identificationen_US
dc.titleComparative Analysis between Genetic Algorithm and Simulated Annealing-Based Frameworks for Optimal Sensor Placement and Structural Health Monitoring Purposesen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.3390/buildings12091383-
dc.identifier.scopus2-s2.0-85138680824-
dc.identifier.urlhttps://api.elsevier.com/content/abstract/scopus_id/85138680824-
dc.contributor.affiliationDepartment of Civil and Environmental Engineeringen_US
dc.contributor.affiliationDepartment of Civil and Environmental Engineeringen_US
dc.description.volume12en_US
dc.description.issue9en_US
dc.date.catalogued2022-10-11-
dc.description.statusPublisheden_US
dc.identifier.openURLhttps://www.mdpi.com/2075-5309/12/9/1383/htmen_US
dc.relation.ispartoftextBuildingsen_US
crisitem.author.parentorgFaculty of Engineering-
crisitem.author.parentorgFaculty of Engineering-
Appears in Collections:Department of Civil and Environmental Engineering
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