ABSTRACT
Geopolymer (GP) concrete is a novel construction material that can be used in place of traditional Portland cement (PC) concrete to reduce greenhouse gas emissions and effectively manage industrial waste. Fly ash (FA) has long been utilized as a key constituent in GPs, and GP technology provides an environmentally benign alternative to FA utilization. As a result, a thorough examination of GP concrete manufactured using FA as a precursor (FA-GP concrete) and employed as a replacement for conventional concrete has become crucial. According to the findings of current investigations, FA-GP concrete has equal or superior mechanical and physical characteristics compared to PC concrete. This article reviews the clean production, mix design, compressive strength (CS), and microstructure (Ms) analyses of the FA-GP concrete to collect and publish the most recent information and data on FA-GP concrete. In addition, this paper shall attempt to develop a comprehensive database based on the previous research study that expounds on the impact of substantial aspects such as physio-chemical characteristics of precursors, mixes, curing, additives, and chemical activation on the CS of FA-GP concrete. The purpose of this work is to give viewers a greater knowledge of the consequences and uses of using FA as a precursor to making effective GP concrete.
ABSTRACT
A variety of ashes used as the binder in geopolymer concrete such as fly ash (FA), ground granulated blast furnace slag (GGBS), rice husk ash (RHA), metakaolin (MK), palm oil fuel ash (POFA), and so on, among of them the FA was commonly used to produce geopolymer concrete. However, one of the drawbacks of using FA as a main binder in geopolymer concrete is that it needs heat curing to cure the concrete specimens, which lead to restriction of using geopolymer concrete in site projects; therefore, GGBS was used as a replacement for FA with different percentages to tackle this problem. In this study, Artificial Neural Network (ANN), M5P-Tree (M5P), Linear Regression (LR), and Multi-logistic regression (MLR) models were used to develop the predictive models for predicting the compressive strength of blended ground granulated blast furnace slag and fly ash based-geopolymer concrete (GGBS/FA-GPC). A comprehensive dataset consists of 220 samples collected in several academic research studies and analyzed to develop the models. In the modeling process, for the first time, eleven effective variable parameters on the compressive strength of the GGBS/FA-GPC, including the Activated alkaline solution to binder ratio (l/b), FA content, SiO2/Al2O3 (Si/Al) of FA, GGBS content, SiO2/CaO (Si/Ca) of GGBS, fine (F) and coarse (C) aggregate content, sodium hydroxide (SH) content, sodium silicate (SS) content, (SS/SH) and molarity (M) were considered as the modeling input parameters. Various statistical assessments such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Scatter Index (SI), OBJ value, and the Coefficient of determination (R2) were used to evaluate the efficiency of the developed models. The results indicated that the ANN model better predicted the compressive strength of GGBS/FA-GPC mixtures compared to the other models. Moreover, the sensitivity analysis demonstrated that the alkaline liquid to binder ratio, fly ash content, molarity, and sodium silicate content are the most affecting parameter for estimating the compressive strength of the GGBS/FA-GPC.
