Prediction compressive strength of Portland cement-based geopolymers by artificial neural networks
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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.
KeywordsArtificial neural networks Geopolymer Portland cement Compressive strength
- 6.Hardjito D, Wallah SE, Sumajouw DMJ, Rangan BV (2004) On the development of fly ash-based geopolymer concrete. ACI Mater J 101(6):467–472Google Scholar
- 9.Muñiz-Villarreal MS, Manzano-Ramírez A, Sampieri-Bulbarela S, Ramón Gasca-Tirado J, Reyes-Araiza JL, Rubio-Ávalos JC, Pérez-Bueno JJ, Apatiga LM, Zaldivar-Cadena A, Amigó-Borrás V (2011) The effect of temperature on the geopolymerisation process of a metakaolin-based geopolymer. Mater Lett 65:995–998CrossRefGoogle Scholar
- 29.ASTM C39 (2001) Standard test method for compressive strength of cylindrical concrete specimens. ASTM, PhiladelphiaGoogle Scholar
- 36.Suratgar AA, Tavakoli MB, Hoseinabadi A (2005) Modified Levenberg–Marquardt method for neural networks training. World Acad Sci Eng Technol 6:46–48Google Scholar