Prediction water absorption resistance of lightweight geopolymers by artificial neural networks
- 255 Downloads
In the present work, water absorption of lightweight geopolymers produced by fine fly ash, rice husk bark ash and palm oil clinker (POC) aggregates has been modeled based on artificial neural networks. To build the models, training, validating and testing using experimental results from 144 specimens were conducted. The data used in the multilayer feed-forward neural networks models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA+RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing and the test trial number. According to these input parameters, in the neural networks models, the water absorption of each specimen was predicted. The best value of R2 was 0.9972 and the minimum value of that was 0.8301. The training, validating and testing results in the neural networks model have shown a strong potential for predicting the water absorption of the geopolymer specimens.
KeywordsGeopolymer Water absorption Ashes mixture Palm oil clinker ANNs
- 17.ASTM C642 (2001) Standard test method for density, absorption, and voids in hardened concrete. ASTM, Philadelphia, PAGoogle Scholar
- 23.Suratgar AA, Tavakoli MB, Hoseinabadi A (2005) Modified Levenberg–Marquardt method for neural networks training. World Acad Sci Eng Technol 6:46–48Google Scholar