ANN models for nano silica/ silica fume concrete strength prediction. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Hodhod, Osama A.; Khalafalla, Mohamed S.; Osman, Mohamed S.M.
• Is Part Of: Water science. Volume 33:Issue 1(2019)
• Page Start: 118
• Page End: 127
• Publication Date: 2019-01-02
• Subjects: Concrete mixing proportions -- nano silica -- silica fume -- compressive strength -- artificial neural network; Water quality management -- Research -- Egypt -- Periodicals; Water resources development -- Research -- Egypt -- Periodicals; Water-supply, Agricultural -- Egypt -- Periodicals; Water-supply -- Research -- Egypt -- Periodicals; Water quality management -- Research; Water resources development -- Research; Water-supply, Agricultural; Water-supply -- Research; Egypt; Periodicals; Electronic journals; 628.1
• DOI: 10.1080/11104929.2019.1669005
• Languages: English
• ISSNs: 1110-4929

Abstract

ABSTRACT: Artificial Neural Network (ANN) modelswere built to expect the compression strength of various types of concrete incorporatingnano silica (NS) and silica fume (SF) as partial cement replacement. The mixtures data used in the networks model, which was collected from previous researchers, studied the effect of NS and SF on concrete. The previous researchers experimentally tested the specimens containing up to 10% of NS and up to 20% of SF as a partial cement replacement at age 7 and 28 days. A total of 488 experiments were used as data sets to train and test the network. The input parameters, like cement content, nano silica, water to cement ratio, and aggregate type and proportion, were varied for each experiment. Three sets of data were modeled using ANNs for both ages 7 and 28 days to predict the strength. The maximum average error of the three models did not exceed 10% of the exact result.