Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In order to predict compressive strength of geopolymers prepared from alumina-silica natural products, based on the effect of Al 2 O 3 /SiO 2, Na 2 O/Al 2 O 3, Na 2 O/H 2 O, and Na/[Na+K], 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.Read More: http://ascelibrary.org/doi/abs/10.1061/(ASCE)MT.1943-5533.0000829
Original languageEnglish
JournalJournal of Materials in Civil Engineering
Volume26
Issue number3
Early online date4 Apr 2013
DOIs
Publication statusPublished - 2013

Keywords

  • Artificial neural network
  • Geopolymers
  • Feedforward network
  • Back-propagation
  • Activated alumina-silica natural products

Fingerprint Dive into the research topics of 'Use of a Neural Network to Predict Strength and Optimum Compositions of Natural Alumina-Silica-Based Geopolymers'. Together they form a unique fingerprint.

  • Cite this