Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/2186
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dc.contributor.authorDoğan, Sevgi Zeynep-
dc.contributor.authorArditi, David-
dc.contributor.authorGünaydın, Hüsnü Murat-
dc.date.accessioned2016-10-07T11:11:27Z
dc.date.available2016-10-07T11:11:27Z
dc.date.issued2006
dc.identifier.citationDoğan, S. Z., Arditi, D., and Günaydın, H. M. (2006). Determining attribute weights in a CBR model for early cost prediction of structural systems. Journal of Construction Engineering and Management, 132(10), 1092-1098. doi:10.1061/(ASCE)0733-9364(2006)132:10(1092)en_US
dc.identifier.issn0733-9364
dc.identifier.issn0733-9364-
dc.identifier.urihttp://doi.org/10.1061/(ASCE)0733-9364(2006)132:10(1092)
dc.identifier.urihttp://hdl.handle.net/11147/2186
dc.description.abstractThis paper compares the performance of three optimization techniques, namely feature counting, gradient descent, and genetic algorithms (GA) in generating attribute weights that were used in a spreadsheet-based case based reasoning (CBR) prediction model. The generation of the attribute weights by using the three optimization techniques and the development of the procedure used in the CBR model are described in this paper in detail. The model was tested by using data pertaining to the early design parameters and unit cost of the structural system of 29 residential building projects. The results indicated that GA-augmented CBR performed better than CBR used in association with the other two optimization techniques. The study is of benefit primarily to researchers as it compares the impact attribute weights generated by three different optimization techniques on the performance of a CBR prediction tool.en_US
dc.language.isoenen_US
dc.publisherAmerican Society of Civil Engineers (ASCE)en_US
dc.relation.ispartofJournal of Construction Engineering and Management - ASCEen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectStructural designen_US
dc.subjectConstruction costsen_US
dc.subjectCost estimatesen_US
dc.subjectDecision makingen_US
dc.titleDetermining attribute weights in a CBR model for early cost prediction of structural systemsen_US
dc.typeArticleen_US
dc.authoridTR114949en_US
dc.authoridTR7988en_US
dc.departmentIzmir Institute of Technology. Architectureen_US
dc.identifier.volume132en_US
dc.identifier.issue10en_US
dc.identifier.startpage1092en_US
dc.identifier.endpage1098en_US
dc.identifier.wosWOS:000240759300009
dc.identifier.scopusSCOPUS:2-s2.0-33748765247
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1061/(ASCE)0733-9364(2006)132:10(1092)-
dc.relation.doi10.1061/(ASCE)0733-9364(2006)132:10(1092)en_US
dc.coverage.doi10.1061/(ASCE)0733-9364(2006)132:10(1092)en_US
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.cerifentitytypePublications-
item.grantfulltextopen-
crisitem.author.deptDepartment of Architecture-
Appears in Collections:Architecture / Mimarlık
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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