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dc.contributor.authorYadollahi, M.M. and Benli, A. and Demirboʇa, R.
dc.date.accessioned2021-04-08T12:08:57Z
dc.date.available2021-04-08T12:08:57Z
dc.date.issued2015
dc.identifier10.1179/1433075X15Y.0000000020
dc.identifier.issn14328917
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84959516818&doi=10.1179%2f1433075X15Y.0000000020&partnerID=40&md5=f5e89be1264ad9a086750289ed6b3e93
dc.identifier.urihttp://acikerisim.bingol.edu.tr/handle/20.500.12898/4732
dc.description.abstractGeopolymers are highly complex materials which involve many variables and make for which modelling the properties is very difficult. There is no systematic approach in mix design for geopolymers. Since the amounts of silicamodulus, Na2O content, w/b ratios and curing time have a great influence on the compressive strength, an ANN (artificial neural network) method has been established for predicting compressive strength of ground pumice based Geopolymers and the possibilities of adapting ANN and artificial intelligence system for predicting the compressive strength have been studied. Consequently, a multilayer ANN by using back propagation architecture can be developed for geopolymer compressive strength prediction. In this study, the coefficient of determination (R2) has been used for investigating the proposedmodel accuracy. As a result, proposed ANNmodel can predict the compressive strength of geopolymer with R2=0.958. © W. S. Maney & Son Ltd 2015.
dc.language.isoEnglish
dc.sourceMaterials Research Innovations
dc.titlePrediction of compressive strength of geopolymer composites using an artificial neural network


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