Comparison of ABC, CPSO, DE and GA Algorithms in FRF Based Structural Damage Identification

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dc.authorid0000-0003-3070-6365en_US
dc.contributor.authorGökdağ, Hakan
dc.date.accessioned2021-03-20T20:15:59Z
dc.date.available2021-03-20T20:15:59Z
dc.date.issued2013
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Makine Mühendisliği Bölümüen_US
dc.description.abstractIn this contribution, performances of well-known population based algorithms, the artificial bee colony (ABC), contemporary particle swarm optimization (CPSO), genetic algorithm (GA), and differential evolution (DE) are compared in a basic model for damage identification (DI). DI is modeled as an inverse problem with the objective function based on the difference of the frequency response functions (FRF) computed by the finite element model of the structure and the reference data measured from damaged structure. Damage parameters are determined solving the problem with the aforementioned algorithms. It was observed that DE is the best one of a given number of function evaluations and gives the most accurate results in spite of noise interference to the reference data. According to the relevant literature, this is the first study including a comparison of these algorithms in an FRF based DI study.en_US
dc.identifier.endpage802en_US
dc.identifier.issn0025-5300
dc.identifier.issue10en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage796en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/1259
dc.identifier.volume55en_US
dc.identifier.wosWOS:000327005400011en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorGökdağ, Hakan
dc.language.isoenen_US
dc.publisherCarl Hanser Verlagen_US
dc.relation.ispartofMaterials Testingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject[No Keywords]en_US
dc.titleComparison of ABC, CPSO, DE and GA Algorithms in FRF Based Structural Damage Identificationen_US
dc.typeArticleen_US

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