Modelling masonry crew productivity using two artificial neural network techniques

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dc.contributor.authorGerek, Ibrahim Halil
dc.contributor.authorErdis, Ercan
dc.contributor.authorMistikoglu, Gulgun
dc.contributor.authorUsmen, Mumtaz
dc.date.accessioned2024-09-18T20:55:28Z
dc.date.available2024-09-18T20:55:28Z
dc.date.issued2015
dc.departmentHatay Mustafa Kemal Üniversitesien_US
dc.description.abstractArtificial neural networks have been effectively used in various civil engineering fields, including construction management and labour productivity. In this study, the performance of the feed forward neural network (FFNN) was compared with radial basis neural network (RBNN) in modelling the productivity of masonry crews. A variety of input factors were incorporated and analysed. Mean absolute percentage error (MAPE) and correlation coefficient (R) were used to evaluate model performance. Research results indicated that the neural computing techniques could be successfully employed in modelling crew productivity. It was also found that successful models could be developed with different combinations of input factors, and several of the models which excluded one or more input factors turned out to be better than the baseline models. Based on the MAPE values obtained for the models, the RBNN technique was found to be better than the FFNN technique, although both slightly overestimated the masons' productivity.en_US
dc.description.sponsorshipTUBITAK (The Scientific and Technical Research Council of Turkey) [106M055]en_US
dc.description.sponsorshipThis paper is based on research work undertaken as part of a larger project (106M055) sponsored by TUBITAK (The Scientific and Technical Research Council of Turkey). This support is gratefully acknowledged. The authors would like to express their gratitude to the other members of the research team (E. Oral, M. Oral, M. E. Ocal, and O. Paydak) for their invaluable contributions to the project.en_US
dc.identifier.doi10.3846/13923730.2013.802741
dc.identifier.endpage238en_US
dc.identifier.issn1392-3730
dc.identifier.issn1822-3605
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-84926417699en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage231en_US
dc.identifier.urihttps://doi.org/10.3846/13923730.2013.802741
dc.identifier.urihttps://hdl.handle.net/20.500.12483/11843
dc.identifier.volume21en_US
dc.identifier.wosWOS:000348656200009en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherVilnius Gediminas Tech Univen_US
dc.relation.ispartofJournal of Civil Engineering and Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectproductivity modellingen_US
dc.subjectcrew productivityen_US
dc.subjectartificial neural networksen_US
dc.subjectconstruction industryen_US
dc.subjectmasonryen_US
dc.titleModelling masonry crew productivity using two artificial neural network techniquesen_US
dc.typeArticleen_US

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