dc.creator | Kovačević, Miljan | |
dc.creator | Ivanišević, Nenad | |
dc.creator | Petronijević, Predrag | |
dc.creator | Despotović, Vladimir | |
dc.date.accessioned | 2021-04-20T11:52:56Z | |
dc.date.available | 2021-04-20T11:52:56Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 0350-2465 | |
dc.identifier.uri | https://grafar.grf.bg.ac.rs/handle/123456789/2336 | |
dc.description.abstract | Seven state-of-the-art machine learning techniques for estimation of construction
costs of reinforced-concrete and prestressed concrete bridges are investigated in this
paper, including artificial neural networks (ANN) and ensembles of ANNs, regression
tree ensembles (random forests, boosted and bagged regression trees), support
vector regression (SVR) method, and Gaussian process regression (GPR). A database
of construction costs and design characteristics for 181 reinforced-concrete and
prestressed-concrete bridges is created for model training and evaluation | sr |
dc.language.iso | en | sr |
dc.publisher | Građevinar | sr |
dc.relation | info:eu-repo/grantAgreement/MESTD/MPN2006-2010/20212/RS// | |
dc.rights | restrictedAccess | sr |
dc.source | Časopis Građevinar | sr |
dc.subject | reinforced concrete bridges | sr |
dc.subject | prestressed concrete bridges | sr |
dc.subject | machine learning | sr |
dc.subject | construction costs | sr |
dc.title | Construction cost estimation of reinforced and prestressed concrete bridges using machine learning | sr |
dc.type | article | sr |
dc.rights.license | ARR | sr |
dc.citation.rank | M23~ | |
dc.citation.volume | 73 | |
dc.identifier.doi | 10.14256/JCE.2738.2019 | |
dc.identifier.wos | 000629004600001 | |
dc.type.version | publishedVersion | sr |