Artificial neural networks for prediction of percentage of water absorption of geopolymers produced by waste ashes
In the present work, percentage of water absorption of geopolymers made from seeded fly ash and rice husk bark ash has been predicted by artificial neural networks. Different specimens, made from a mixture of fly ash and rice husk bark ash in fine and coarse form together with alkali activatormade of water glass and NaOH solution, were subjected to permeability tests at 7 and 28 days of curing. The curing regime was different: one set cured at room temperature until reaching to 7 and 28 days and the other sets were oven cured for 36 h at a range of 40–90 °C and then cured at room temperature for 7 and 28 days. To build the model, training and testing using experimental results from 120 specimens were conducted. According to these input parameters, in the neural networks model, the percentage of water absorption of each specimen was predicted. The training and testing results in the neural networks model have shown a strong potential for predicting the percentage of water absorption of the geopolymer specimens.
Volume 43, 2020
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