Artificial neural network design for behaviours of reinforced concrete column under axial load and comparison of experimental study

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dc.contributor.authorÇöğürcü, Mustafa Tolga
dc.contributor.authorSarıtaş, İsmail
dc.contributor.authorAltın, Mustafa
dc.contributor.authorDöndüren, M. Sami
dc.contributor.authorKamanlı, Mehmet
dc.contributor.authorKaltakcı, M. Yaşar
dc.date.accessioned2020-03-26T17:28:45Z
dc.date.available2020-03-26T17:28:45Z
dc.date.issued2008
dc.departmentSelçuk Üniversitesien_US
dc.description9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing, CompSysTech'08 -- 12 June 2008 through 13 June 2008 -- Gabrovo -- 76845en_US
dc.description.abstractConstruction (building) sector is one of the sectors which adapt the rapid development of the technology. Especially in frame (skeleton) systems, very serious studies on columns that are vertical conveyors (carriers) are being done. Generally, these studies are implemented by experimentally and computer software is used in these studies as they offer good results. Artificial neural network (ANN) has been used in several engineering application areas including civil engineering. The use of ANN to predict the behaviour of reinforced concrete (R/C) members, using the vast amount of experimental data as a test-bed for learning and verification of results, proved to be available method for carrying out parametric studies. In the present study, columns under the axial load were manufactured from concrete that has been produced without carrying out the standards. Load conveying capacities and tension-unit deformation relationships of columns manufactured with different geometrical and equipment (outfit) properties and same cross-section area have been investigated. At the same time, research has been done via modelling by using ANN that is one of techniques of artificial intelligence and gained importance in recent years. SPSS statistical packet program is used to evaluate the results of this research. After comparisons of results obtained in the experiments, it has been determined that square column samples have the most axial load conveying capacity. It has also been determined that number of displacement is less in samples of columns wrapped (winded) with fret comparing to samples of columns wrapped with stirrup. The same results are obtained after modelling by ANN. As the result of statistical analyses that have been done in %5 reliability interval between data obtained from experiments and ANN, it has been seen that ANN can be used as reliable method.en_US
dc.identifier.doi10.1145/1500879.1500926en_US
dc.identifier.isbn9.78955E+12
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://dx.doi.org/10.1145/1500879.1500926
dc.identifier.urihttps://hdl.handle.net/20.500.12395/22844
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.relation.ispartofProceedings of the 9th International Conference on Computer Systems and Technologies and Workshop for PhD Students in Computing, CompSysTech'08en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networken_US
dc.subjectAxial loaden_US
dc.subjectColumnsen_US
dc.subjectConcreteen_US
dc.subjectDisplacementen_US
dc.titleArtificial neural network design for behaviours of reinforced concrete column under axial load and comparison of experimental studyen_US
dc.typeConference Objecten_US

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