The building industry, which emits a significant quantity of greenhouse gases, is under tremendous pressure due to global climate change and its consequences for communities. Given the environmental issues associated with cement production, geopolymer concrete has emerged as a sustainable construction material. Geopolymer concrete is an eco-friendly construction material that uses industrial or agricultural by-product ashes as the principal binder instead of Portland cement. Fly ash, ground granulated blast furnace slag, rice husk ash, metakaolin, and palm oil fuel ash were all employed as binders in geopolymer concrete, with fly ash being the most frequent. The most important engineering property for all types of concrete composites, including geopolymer concrete, is the compressive strength. It is influenced by different parameters such as the chemical composition of the binder materials, alkaline liquid to binder ratio, extra water content, superplasticizers dosages, binder content, fine and coarse aggregate content, sodium hydroxide and sodium silicate content, the ratio of sodium silicate to sodium hydroxide, the concentration of sodium hydroxide (molarity), curing temperature, curing durations inside oven, and specimen ages. In order to demonstrate the effects of these varied parameters on the compressive strength of the fly ash-based geopolymer concrete, a comprehensive dataset of 800 samples was gathered and analyzed. According to the findings, the curing temperature, sodium silicate content, and alkaline solution to binder ratio are the most significant independent parameters influencing the compressive strength of the fly ash-based geopolymer concrete (FA-BGPC) composites.

Mechanical properties and data analysis for the prediction of different mechanical properties of geopolymer concrete (GPC) were investigated. A relatively large amount of test data from 126 past works was collected, analyzed, and correlation between different mechanical properties and compressive strength was investigated. Equations were proposed for the properties of splitting tensile strength, flexural strength, modulus of elasticity, Poisson’s ratio, and strain corresponding to peak compressive strength. The proposed equations were found accurate and can be used to prepare a state-of-art report on GPC. Based on data analysis, it was found that there is a chance to apply some past proposed equations for predicting different mechanical properties. CEB-FIP equations for the prediction of splitting tensile strength and strain corresponding to peak compressive stress were found to be accurate, while ACI 318 equations for splitting tensile and elastic modulus overestimates test data for GPC of low compressive strength.

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.