Compressive strength of geopolymer concrete composites: a systematic comprehensive review, analysis and modeling

Abstract

The desire to make the concrete industry more environmentally friendly has existed for a long time. Geopolymer concrete, which uses industrial or agricultural by-product ashes as the primary source of binder materials instead of Portland cement, has emerged as a viable building material due to the environmental concerns associated with cement production. One of the most important mechanical parameters for all types of concrete composites, including geopolymer concrete, is compressive strength. This parameter is influenced by a variety of factors, including the alkaline solution to binder ratio, the type and amount of binder, the chemical composition of the binder materials, the amount of aggregate present, the type and amount of alkaline solutions, the ratio of alkaline liquid to binder materials, the curing regime, and the age of the specimens. In this context, a detailed systematic assessment was conducted to demonstrate the effect of these various parameters on the compressive strength of fly ash-based geopolymer concrete (FA-GPC). In addition, multi-scale models such as artificial neural networks, M5P-tree, linear regression, and multi-logistic regression models were developed to predict the compressive strength of FA-GPC composites. Results show that the curing temperature (between 60 °C to 90 °C), sodium silicate to sodium hydroxide ratio (between 1.5 to 2.5), and the alkaline solution to the binder ratio (between 0.35 to 0.5) are those parameters that govern the compressive strength of the FA-GPC. Furthermore, based on the statistical assessment tools, the ANN model has better performance for predicting the compressive strength of FA-GPC than the other developed models as it has the highest value of the coefficient of determination (0.96), lower values of the root mean squared error (3.33), mean absolute error (2.58), objective function value (2.91), and scatter index (0.109).

Graphical Abstract

Keywords:

• Geopolymer concrete
• mix proportion
• compressive strength
• analysis
• ANN
• M5P-tree model

Disclosure statement

No potential conflict of interest was reported by the authors.