Enhanced Predictive Models for Construction Costs: A Case Study of Turkish Mass Housing Sector

Basit Öğe Kaydı
dc.contributor.authorUğur, Latif Onur
dc.contributor.authorKanıt, Recep
dc.contributor.authorErdal, Hamit
dc.contributor.authorNamlı, Ersin
dc.contributor.authorErdal, Halil İbrahim
dc.contributor.authorBaykan, Umut Naci
dc.contributor.authorErdal, Mürsel
dc.date.accessioned2020-05-01T12:11:53Z
dc.date.available2020-05-01T12:11:53Z
dc.date.issued2019
dc.departmentDÜ, Teknoloji Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.descriptionErdal, Mursel/0000-0002-9338-6162; Namli, Ersin/0000-0001-5980-9152en_US
dc.descriptionWOS: 000463791300006en_US
dc.description.abstractThe analysis of a construction project, regarding cost, is one of the most vital problem in planning. Due to its nature, the construction sector is an area of strong competition and estimation works are of vital importance. In recent years the Turkish Republic has started a serious urban regeneration movement in parallel to its economic development. This study is based on the drawings and quantities of 63 detached multi-story reinforced concrete housing unit projects of the Housing Development Administration (TOKI) and the Turkey Residential Building Cooperative Union (TURKKONUT). TOKI is a public company and its projects are that have been applied to 282 separate projects and are being applied to a further 266. On the other side TURKKONUT is a union of 1347 private building cooperative and have been completed 200,000 residential building. The main objective of this study is to improve the estimation accuracy of individual machine learning techniques, namely multi-layer perceptron and classification and regression trees and compares the performance of two machine learning meta-algorithms (i.e., bagging and random subspace) on a real world construction cost estimation problem. The study shows that the estimation accuracy of ensemble models are better than the models that constructed by their base learners and ensemble models may improve individual machine learning models.en_US
dc.identifier.doi10.1007/s10614-018-9814-9en_US
dc.identifier.endpage1419en_US
dc.identifier.issn0927-7099
dc.identifier.issn1572-9974
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage1403en_US
dc.identifier.urihttps://doi.org/10.1007/s10614-018-9814-9
dc.identifier.urihttps://hdl.handle.net/20.500.12684/6236
dc.identifier.volume53en_US
dc.identifier.wosWOS:000463791300006en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofComputational Economicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBaggingen_US
dc.subjectCARTen_US
dc.subjectMulti-layer perceptronen_US
dc.subjectEnsemble machine learningen_US
dc.subjectRandom subspaceen_US
dc.subjectConstruction projecten_US
dc.subjectConstruction costen_US
dc.titleEnhanced Predictive Models for Construction Costs: A Case Study of Turkish Mass Housing Sectoren_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
6236.pdf
Boyut:
672.01 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Makale Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu