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.accessioned2025-01-06T17:44:04Z
dc.date.available2025-01-06T17:44:04Z
dc.date.issued2015
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.
dc.description.sponsorshipTUBITAK (The Scientific and Technical Research Council of Turkey) [106M055]
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.
dc.identifier.doi10.3846/13923730.2013.802741
dc.identifier.endpage238
dc.identifier.issn1392-3730
dc.identifier.issn1822-3605
dc.identifier.issue2
dc.identifier.scopus2-s2.0-84926417699
dc.identifier.scopusqualityQ1
dc.identifier.startpage231
dc.identifier.urihttps://doi.org/10.3846/13923730.2013.802741
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2917
dc.identifier.volume21
dc.identifier.wosWOS:000348656200009
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherVilnius Gediminas Tech Univ
dc.relation.ispartofJournal of Civil Engineering and Management
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectproductivity modelling
dc.subjectcrew productivity
dc.subjectartificial neural networks
dc.subjectconstruction industry
dc.subjectmasonry
dc.titleModelling masonry crew productivity using two artificial neural network techniques
dc.typeArticle

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