Prediction compressive strength of lightweight geopolymers by ANFIS

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 adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk bark ash with and without POC were subjected to compressive strength tests at 2, 7 and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7 and 28 days. Addition of POC to the geopolymeric mixtures caused reduced strength at all ages of curing. However a considerable increase in strength to weight ratio was acquired for the specimen with a high content of fine POC particles at 28 days of curing. To build the ANFIS 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 ANFIS models, 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.