Prediction of compressive strength of geopolymer concrete using machine learning techniques

Abstract

AbstractGeopolymer concrete (GPC) is the result of an inorganic polymerization reaction that takes place in presence of an alkaline medium in the materials such as fly ash and slag, which are rich in silicates and aluminates. In this study, an artificial neural network (ANN), multiple linear regression, and the multivariate nonlinear regression (MNLR) models were designed to predict the 28 days compressive strength of the GPC. To train the models, a total of 289 data sets were used, which were published by different researchers in the open literature. The input parameters, namely, binder content, amount of slag, rest period, curing temperature, curing period, the ratio of NaOH/Na2SiO3, amount of superplasticizer, extra water added, the molarity of NaOH, alkaline activator to binder ratio, amount of coarse, and fine aggregate were used. The performance of the ANN model was better than the multi linear regression (MLR) and multi non‐linear regression (MNLR) models to estimate the strength. The sensitivity analysis on ANN was also performed to study the influence of each parameter. The sensitivity analysis shows that the ratio of NaOH/Na2SiO3 significantly affects the compressive strength of GPC.