Paper Info
Title
Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks
Abstract
In the present study, compressive strength results of geopolymers produced by ordinary Portland cement (OPC) as aluminosilicate source have been modeled by artificial neural networks. Six main factors including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time and water curing regime each at 4 levels were considered for designing. A total of 32 experiments were conducted according to the L32 array proposed by the method. The neural network models were constructed by 10 input parameters including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time, water curing regime, water glass content, NaOH content, Portland cement content and test trial number. The value for the output layer was the compressive strength. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. The results indicate that artificial neural networks model is a powerful tool for predicting the compressive strength of the geopolymers in the considered range.
Year
DOI
Venue
2019
10.1007/s00521-012-1082-3
Neural Computing and Applications
Keywords
Field
DocType
Artificial neural networks, Geopolymer, Portland cement, Compressive strength
Portland cement,Mathematical optimization,Composite material,Aluminosilicate,Compressive strength,Geopolymer,Curing (food preservation),Artificial neural network,Cement,Mathematics
Journal
Volume
Issue
ISSN
31
2
1433-3058
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Ali Nazari128.58
hadi hajiallahyari200.34
ali rahimi310.70
hamid khanmohammadi400.34
m amini500.34