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Application of Artificial Neural Networks for Prediction of the Strength Properties of CSG Materials

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dc.contributor.author임정열-
dc.contributor.author김기영-
dc.contributor.author문홍득-
dc.contributor.authorGuangri Jin-
dc.date.accessioned2022-12-26T17:31:05Z-
dc.date.available2022-12-26T17:31:05Z-
dc.date.issued2018-
dc.identifier.issn1598-0820-
dc.identifier.issn2714-1233-
dc.identifier.urihttps://scholarworks.gnu.ac.kr/handle/sw.gnu/12187-
dc.description.abstractThe number of researches on the mechanical properties of cemented sand and gravel (CSG) materials and the application of the CSG Dam has been increased. In order to explain the technical scheme of strength prediction model about the artificial neural network, we obtained the sample data by orthogonal test using the PVA (Polyvinyl alcohol) fiber, different amount of cementing materials and age, and established the efficient evaluation and prediction system. Combined with the analysis about the importance of influence factors, the prediction accuracy was above 95%. This provides the scientific theory for the further application of CSG, and will also be the foundation to apply the artificial neural network theory further in water conservancy project for the future.-
dc.format.extent10-
dc.language영어-
dc.language.isoENG-
dc.publisher한국지반환경공학회-
dc.titleApplication of Artificial Neural Networks for Prediction of the Strength Properties of CSG Materials-
dc.title.alternativeApplication of Artificial Neural Networks for Prediction of the Strength Properties of CSG Materials-
dc.typeArticle-
dc.publisher.location대한민국-
dc.identifier.doi10.14481/jkges.2018.19.5.13-
dc.identifier.bibliographicCitation한국지반환경공학회 논문집, v.19, no.5, pp 13 - 22-
dc.citation.title한국지반환경공학회 논문집-
dc.citation.volume19-
dc.citation.number5-
dc.citation.startPage13-
dc.citation.endPage22-
dc.identifier.kciidART002342191-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorC.S.G (Cemented Sand and Gravel)-
dc.subject.keywordAuthorArtificial neural network-
dc.subject.keywordAuthorInfluence factors-
dc.subject.keywordAuthorStrength-
dc.subject.keywordAuthorPrediction model-
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건설환경공과대학 (건설시스템공학과)
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