ANFIS-based prediction of the compressive strength of geopolymers with seeded fly ash and rice huskbark ash
In the present work, compressive strength of geopolymers made from seeded fly ash and rice huskbark ash has been predicted by adaptive network-based fuzzy inference systems (ANFIS). Different specimens, made from a mixture of fly ash and rice huskbark ash in fine and coarse forms and a mixture of water glass and NaOH mixture as alkali activator, were subjected to compressive strength tests at 7 and 28days of curing. The curing regimes were different: one set of the specimens were cured in water at room temperature until 7 and 28days and the other sets were oven-cured for 36h at the range of 4090C and then cured at room temperature until 7 and 28days. A model based on ANFIS for predicting the compressive strength of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The used data as the inputs of ANFIS models are arranged in a format of six parameters that cover the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice huskbark ash in the ashes mixture, the percentage of coarse rice huskbark ash in the ashes mixture, the temperature of curing, and the time of water curing. According to these input parameters in the ANFIS models, the compressive strength of each specimen was predicted. The training and testing results in ANFIS models showed a strong potential for predicting the compressive strength of the geopolymeric specimens.
anfis compressive strength geopolymer fly ash rice huskbark ash curing regime ftir