Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks

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dc.contributor.authorJuszczyk, Michałcs
dc.contributor.authorHanák, Tomášcs
dc.contributor.authorVýskala, Miloslavcs
dc.contributor.authorPacyno, Hannacs
dc.contributor.authorSiejda, Michalcs
dc.coverage.issue23cs
dc.coverage.volume13cs
dc.date.accessioned2024-02-22T10:45:22Z
dc.date.available2024-02-22T10:45:22Z
dc.date.issued2023-11-28cs
dc.description.abstracthis paper presents research results on the development of an original cost prediction model for construction costs in sewerage projects. The focus is placed on fast cost estimates applicable in the early stages of a project, based on fundamental information available during the initial design phase of sanitary sewers prior to the detailed design. The originality and novelty of this research lie in the application of artificial neural network ensembles, which include a combination of several individual neural networks and the use of simple averaging and generalized averaging approaches. The research resulted in the development of two ensemble-based models, including five neural networks that were trained and tested using data collected from 125 sewerage projects completed in the Czech Republic between 2018 and 2022. The data included information relevant to various aspects of projects and contract costs, updated to account for changes in costs over time. The developed models present satisfactory predictive performance, especially the ensemble model based on simple averaging, which offers prediction accuracy within the range of ±30% (in terms of percentage errors) for over 90% of the training and testing samples. The developed models, based on the ensembles of neural networks, outperformed the benchmark model based on the classical approach and the use of multiple linear regression.en
dc.formattextcs
dc.format.extent1-24cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationApplied Sciences - Basel. 2023, vol. 13, issue 23, p. 1-24.en
dc.identifier.doi10.3390/app132312744cs
dc.identifier.issn2076-3417cs
dc.identifier.orcid0000-0002-7820-6848cs
dc.identifier.orcid0000-0003-4179-1630cs
dc.identifier.other185648cs
dc.identifier.researcheridE-3948-2019cs
dc.identifier.scopus55170341300cs
dc.identifier.urihttps://hdl.handle.net/11012/245171
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofApplied Sciences - Baselcs
dc.relation.urihttps://www.mdpi.com/2076-3417/13/23/12744cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2076-3417/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectsewerage projecten
dc.subjectsanitary sewer networksen
dc.subjectconstruction costsen
dc.subjectconstruction projecten
dc.subjectearly cost estimatesen
dc.subjectfast cost estimatesen
dc.subjectneural networks ensemblesen
dc.subjectartificial intelligenceen
dc.titleEarly Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networksen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-185648en
sync.item.dbtypeVAVen
sync.item.insts2024.02.22 11:45:22en
sync.item.modts2024.02.22 11:13:37en
thesis.grantorVysoké učení technické v Brně. Fakulta stavební. Ústav stavební ekonomiky a řízenícs
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