Publication:
Compressive strength estimation of concrete containing zeolite and diatomite: An expert system implementation

dc.contributor.authorKoçak, Yılmaz
dc.contributor.authorGülbandılar, Eyyüp
dc.contributor.buuauthorÖzcan, Giyasettin
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentBilgisayar Mühendisliği Bölümü
dc.contributor.orcid0000-0002-1166-5919
dc.contributor.researcheridZ-1130-2018
dc.contributor.scopusid15770103700
dc.date.accessioned2023-11-17T08:44:35Z
dc.date.available2023-11-17T08:44:35Z
dc.date.issued2018-01
dc.description.abstractIn this study, we analyze the behavior of concrete which contains zeolite and diatomite. In order to achieve the goal, we utilize expert system methods. The utilized methods are artificial neural network and adaptive network-based fuzzy inference systems. In this respect, we exploit seven different mixes of concrete. The concrete mixes contain zeolite, diatomite, mixture of zeolite and diatomite. All seven concrete mixes are exposed to 28, 56 and 90 days' compressive strength experiments with 63 specimens. The results of the compressive strength experiments are used as input data during the training and testing of expert system methods. In terms of artificial neural network and adaptive network-based fuzzy models, data format comprises seven input parameters, which are; the age of samples (days), amount of Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer. On the other hand, the output parameter is defined as the compressive strength of concrete. In the models, training and testing results have concluded that both expert system model yield thrilling medium to predict the compressive strength of concrete containing zeolite and diatomite.
dc.description.sponsorshipDüzce Üniversitesi - 2011.03.HD.011
dc.identifier.citationÖzcan, G. vd. (2018). ''Compressive strength estimation of concrete containing zeolite and diatomite: An expert system implementation''. Computers and Concrete, 21(1), 21-30.
dc.identifier.endpage30
dc.identifier.issn1598-8198
dc.identifier.issn1598-818X
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85058979329
dc.identifier.startpage21
dc.identifier.urihttps://doi.org/10.12989/cac.2018.21.1.021
dc.identifier.urihttps://www.dbpia.co.kr/Journal/articleDetail?nodeId=NODE10763259
dc.identifier.urihttp://hdl.handle.net/11452/34931
dc.identifier.volume21
dc.identifier.wos000429256700003
dc.indexed.wosSCIE
dc.language.isoen
dc.publisherTechno Press
dc.relation.collaborationYurt içi
dc.relation.journalComputers and Concrete
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectComputer science
dc.subjectConstruction & building technology
dc.subjectEngineering
dc.subjectMaterials science
dc.subjectExpert systems
dc.subjectCompressive strength
dc.subjectConcrete
dc.subjectZeolite
dc.subjectDiatomite
dc.subjectArtificial neural-networks
dc.subjectModel tree algorithm
dc.subjectFly-ash
dc.subjectFuzzy-logic
dc.subjectSilica fume
dc.subjectHydration characteristics
dc.subjectMechanical-properties
dc.subjectBlended cements
dc.subjectPortland-cement
dc.subjectPrediction
dc.subjectConcrete mixers
dc.subjectConcrete mixtures
dc.subjectConcretes
dc.subjectExpert systems
dc.subjectFuzzy inference
dc.subjectFuzzy logic
dc.subjectFuzzy neural networks
dc.subjectPortland cement
dc.subjectZeolites
dc.subjectAdaptive network based fuzzy inference system
dc.subjectAdaptive networks
dc.subjectCompressive strength of concrete
dc.subjectDiatomite
dc.subjectOutput parameters
dc.subjectSystem implementation
dc.subjectSystem modeling
dc.subjectTraining and testing
dc.subject.scopusCompressive Strength; High Performance Concrete; Prediction
dc.subject.wosComputer science, interdisciplinary applications
dc.subject.wosConstruction & building technology
dc.subject.wosEngineering, civil
dc.subject.wosMaterials science, characterization & testing
dc.titleCompressive strength estimation of concrete containing zeolite and diatomite: An expert system implementation
dc.typeArticle
dc.wos.quartileQ3 (Computer science, interdisciplinary applications)
dc.wos.quartileQ2
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Bilgisayar Mühendisliği Bölümü
local.indexed.atScopus
local.indexed.atWOS

Files

License bundle

Now showing 1 - 1 of 1
Placeholder
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: