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공종별 공사비와 영향요인의 상관도 분석을 통한 건축 공사비 예측 성능 분석

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dc.contributor.author강윤호-
dc.contributor.author이하늘-
dc.contributor.author김환용-
dc.contributor.author윤석헌-
dc.date.accessioned2023-07-24T04:45:35Z-
dc.date.available2023-07-24T04:45:35Z-
dc.date.issued2023-06-
dc.identifier.issn2508-4003-
dc.identifier.issn2508-402X-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/59885-
dc.description.abstractAlthough effectively managing construction costs is crucial for large-scale and high-rise con struction projects, achieving efficiency and accuracy can be challenging. To improve the accu racy of the construction cost prediction model, this study presents an optimal combination of influential factors based on a correlation analysis of construction costs by type. The study estab lishes five models, including the top three models and the total area obtained through correla tion analysis for each construction type, and utilizes the artificial neural network method to predict construction costs and compare the predictive performance of each model. Analysis shows that the combination of factors with vertical characteristics of the building yields a lower average error rate than other influencing factors. This finding is expected to help improve the combination of influencing factors for construction cost prediction models in future studies.-
dc.format.extent9-
dc.language한국어-
dc.language.isoKOR-
dc.publisher한국CDE학회-
dc.title공종별 공사비와 영향요인의 상관도 분석을 통한 건축 공사비 예측 성능 분석-
dc.title.alternativePerformance Analysis of Construction Cost Prediction through Correlation Analysis of Construction Cost and Influencing Factors by Construction Type-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.bibliographicCitation한국CDE학회 논문집, v.28, no.2, pp 156 - 164-
dc.citation.title한국CDE학회 논문집-
dc.citation.volume28-
dc.citation.number2-
dc.citation.startPage156-
dc.citation.endPage164-
dc.identifier.kciidART002963521-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorConstruction type-
dc.subject.keywordAuthorConceptual cost estimate-
dc.subject.keywordAuthorInfluencing factors-
dc.subject.keywordAuthorMachine learning-
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공학계열 > 건축공학과 > Journal Articles

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