Neural Computing and Applications

, Volume 31, Supplement 2, pp 733–741 | Cite as

Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks

  • Ali NazariEmail author
  • Hadi Hajiallahyari
  • Ali Rahimi
  • Hamid Khanmohammadi
  • Mohammad Amini
Original Article


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.


Artificial neural networks Geopolymer Portland cement Compressive strength 


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Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  • Ali Nazari
    • 1
    Email author
  • Hadi Hajiallahyari
    • 1
  • Ali Rahimi
    • 1
  • Hamid Khanmohammadi
    • 1
  • Mohammad Amini
    • 1
  1. 1.Department of Materials Engineering, Saveh BranchIslamic Azad UniversitySavehIran

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