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Erschienen in:
01.09.2013 | Original Article
RETRACTED ARTICLE: Fuzzy logic-based prediction of compressive strength of lightweight geopolymers
Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2013
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Abstract
In the present work, compressive strength of lightweight inorganic polymers (geopolymers) produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled based on fuzzy logic. To build the model, training, validating and testing were conducted using experimental results from 144 specimens. The used data in the ANFIS models were 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 model, the compressive strength of each specimen was predicted. The training, validating and testing results in the model have shown a strong potential for predicting the compressive strength of the geopolymer specimens in the considered range.