Utilizing ANFIS for prediction water absorption of lightweight geopolymers produced from waste materials

In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by 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 water absorption 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. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of “the percentage of water absorption” to “weight” of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS 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, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens.

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