Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers
Publication: Journal of Materials in Civil Engineering
Volume 26, Issue 3
Abstract
In order to predict compressive strength of geopolymers prepared from alumina-silica natural products, based on the effect of , , , and , more than 50 pieces of data were gathered from the literature. The data was utilized to train and test a multilayer artificial neural network (ANN). Therefore a multilayer feedforward network was designed with chemical compositions of alumina silicate and alkali activators as inputs and compressive strength as output. In this study, a feedforward network with various numbers of hidden layers and neurons were tested to select the optimum network architecture. The developed three-layer neural network simulator model used the feedforward back propagation architecture, demonstrated its ability in training the given input/output patterns. The cross-validation data was used to show the validity and high prediction accuracy of the network. This leads to the optimum chemical composition and the best paste can be made from activated alumina-silica natural products using alkaline hydroxide, and alkaline silicate. The research results are in agreement with mechanism of geopolymerization.
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Information & Authors
Information
Published In
Journal of Materials in Civil Engineering
Volume 26 • Issue 3 • March 2014
Pages: 499 - 503
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Nov 19, 2012
Accepted: Apr 2, 2013
Published online: Apr 4, 2013
Discussion open until: Sep 4, 2013
Published in print: Mar 1, 2014
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