Developing a new mutation operator to solve the RC deep beam problems by aid of genetic algorithm

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dc.contributor.authorKaya, Mustafa
dc.date.accessioned13.07.201910:50:10
dc.date.accessioned2019-07-16T09:16:51Z
dc.date.available13.07.201910:50:10
dc.date.available2019-07-16T09:16:51Z
dc.date.issued2018
dc.departmentMühendislik Fakültesi
dc.description.abstractDue to the fact that the ratio of their height to their openings is very large compared to normal beams, there are difficulties in the design and analysis of deep beams, which differ in behavior. In this study, the optimum horizontal and vertical reinforcement diameters of 5 different beams were determined by using genetic algorithms (GA) due to the openness/height ratio (L/h), loading condition and the presence of spaces in the body. In this study, the effect of different mutation operators and improved double times sensitive mutation (DTM) operator on GAs performance was investigated In the study following random mutation (RM), boundary mutation (BM), non-uniform random mutation (NRM), Makinen, Periaux and Toivanen (MPT) mutation, power mutation (PM), polynomial mutation (PNM), and developed DTM mutation operators were applied to five deep beam problems were used to determine the minimum reinforcement diameter. The fitness values obtained using developed DTM mutation operator was higher than obtained from existing mutation operators. Moreover; obtained reinforcement weight of the deep beams using the developed DTM mutation operator lower than obtained from the existing mutation operators. As a result of the analyzes, the highest fitness value was obtained from the applied double times sensitive mutation (DTM) operator. In addition, it was found that this study, which was carried out using GAs, contributed to the solution of the problems experienced in the design of deep beams.
dc.identifier.doi10.12989/cac.2018.22.5.493
dc.identifier.endpage500en_US
dc.identifier.issn1598-8198
dc.identifier.issn1598-818X
dc.identifier.issue5en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage493en_US
dc.identifier.urihttps://doi.org/10.12989/cac.2018.22.5.493
dc.identifier.urihttps://hdl.handle.net/20.500.12451/4663
dc.identifier.volume22en_US
dc.identifier.wosWOS:000451827500006
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTechno-Press
dc.relation.ispartofComputers and Concrete
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectEvolutionary Slgorithms
dc.subjectArtificial Intelligence
dc.subjectGenetic Algorithms
dc.subjectMutation Operator
dc.subjectDeep Beam
dc.titleDeveloping a new mutation operator to solve the RC deep beam problems by aid of genetic algorithm
dc.typeArticle

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