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dc.contributor.authorShahmansouria, Amir Ali
dc.contributor.authorYazdani, Maziar
dc.contributor.authorGhanbari, Saeed
dc.contributor.authorAkbarzadeh Bengar, Habib
dc.contributor.authorJafari, Abouzar
dc.contributor.authorFarrokh Ghatte, Hamid
dc.date.accessioned2020-12-11T09:13:21Z
dc.date.available2020-12-11T09:13:21Z
dc.date.issued2020
dc.identifier.citationShahmansouria, A. A., Yazdani, M., Ghanbari, S., Akbarzadeh Bengar, H., Jafari, A. & Farrokh Ghatte, H. (2020). Artificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeolite. Journal of Cleaner Production, 279.en_US
dc.identifier.issn0959-6526
dc.identifier.urihttp://hdl.handle.net/20.500.12566/568
dc.description.abstractThe growing concern about global climate change and its adverse impacts on societies is putting severe pressure on the construction industry as one of the largest producers of greenhouse gases. Given the environmental issues associated with cement production, Geopolymer Concrete (GPC) has emerged as a sustainable construction material. This research experimentally studied the effect of partially substituting ground granulated blast-furnace slag (GGBS) with silica fume (SF) and natural zeolite (NZ) (by 0–30% with 5% increments) in the GPC activated by sodium hydroxide (NaOH) solution with different concentrations (4, 6 and 8 M) and sodium silicate (water glass) solution on the compressive strength. Obtained results revealed that increasing the NaOH concentration reduced the concrete strength, while adding SF and NZ to the concrete yielded an improvement in the compressive strength. Moreover, this study proposed an Artificial Neural Network (ANN) to predict the compressive strength of pozzolanic GPC based on GGBS (i.e., at the ages of 7, 28, and 90 days). The compressive strength of GGBS-based GPC (i.e., 117 concrete specimens manufactured out of 39 various mixtures) obtained by experimental tests was used to develop the model. The specimen age, NaOH concentration, contents of NZ, SF, and GGBS were considered as inputs variables for developing the ANN model. The predicted results establish the accuracy and high prediction ability of the proposed model. The findings of this study can bring significant benefits for the range of organizations involved.en_US
dc.description.sponsorshipNo sponsoren_US
dc.language.isoengen_US
dc.publisherJournal of Cleaner Productionen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGeopolymer concreteen_US
dc.subjectJeopolimer betontr_TR
dc.subjectGround granulated blast-furnace slagen_US
dc.subjectÖğütülmüş yüksek fırın cürufutr_TR
dc.subjectSilica fumeen_US
dc.subjectSilika dumanıtr_TR
dc.subjectNatural zeoliteen_US
dc.subjectDoğal zeolittr_TR
dc.subjectCompressive strengthen_US
dc.subjectBasınç dayanımıtr_TR
dc.subjectArtificial neural networken_US
dc.subjectYapay sinir ağıtr_TR
dc.titleArtificial neural network model to predict the compressive strength of eco-friendly geopolymer concrete incorporating silica fume and natural zeoliteen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.relation.publicationcategoryInternational publicationen_US
dc.identifier.scopus2-s2.0-85091123741
dc.contributor.orcid0000-0003-3237-0279 [Farrokh Ghatte, Hamid]
dc.contributor.abuauthorFarrokh Ghatte, Hamid
dc.contributor.yokid296319 [Farrokh Ghatte, Hamid]
dc.contributor.ScopusAuthorID57217443956 [Farrokh Ghatte, Hamid]
dc.identifier.doihttps://doi.org/10.1016/j.jclepro.2020.123697


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