A genetic programming predictive model for parametric study of factors affecting strength of geopolymers

Abstract

In this paper, the effect of different factors including mixture proportions and curing conditions on the compressive strength of fly ash-based geopolymers was studied. Several parameters were used to construct a predictive model based on genetic programming, which delivers the compressive strength of specimens with reasonable accuracy. A parametric study was carried out to evaluate the effect of each individual parameter on the strength of the geopolymers. The results obtained by the model showed that changing the percentage of aggregates in the standard range, and age of curing are ineffective on the compressive strength of the considered geopolymers. On the other hand, increasing the percentage of fly ash, curing temperature and liquid to ash weight ratio were shown to improve the compressive strength. Another important parameter namely, sodium silicate to alkali hydroxide weight ratio had an optimum value of 2.5 to deliver the highest strength. All of the model predictions were in accordance with the experimental results and those available in the literature for many types of fly ash-based geopolymers. It was concluded that fly ash (sourced from Sarawak, Malaysia) can be suitably used to synthesize geopolymers when the producing factors are precisely determined.