Influence of alkali molarity on compressive strength of high-strength geopolymer concrete using machine learning techniques based on curing regimes and temperature

Abstract

The compressive strength behavior of high-strength geopolymer concrete (HSGPC) has been studied in this research work with varying alkali concentration using the novel machine learning techniques. The alkali concentration in the activation solution plays a significant role in the geopolymerization process and affects the resulting compressive strength. In this research work, the range between 4 M and 16 M for alkali molarity (M), 18 kg/m3 and 160 kg/m3 for NaOH and 41 kg/m3 and 229 kg/m3 for NaSi was collected from literature and used in the various design mixes of this exercise. This was necessary because higher alkali concentrations promote a more efficient dissolution and activation of the aluminosilicate compounds, leading to increased geopolymerization and the formation of more calcium silicate hydrate (C-S-H) gel. The increased C-S-H gel content contributes to improved strength development. However, there is an optimal alkali concentration range for the sustainable production of geopolymer concrete, and exceeding this range can have a negative impact on compressive strength and ecofriendly handling of concrete. A total of fifty-three records were collected from literature and deployed in modeling the compressive strength (Fc) considering various curing regimes. Three symbolic machine learning techniques such as genetic programming (GP), evolutionary polynomial regression (EPR), and the artificial neural network (ANN) are used in this research model. The relative importance values for each input parameter were also evaluated, which indicated that all factors have significant impacts on (Fc), but Age (i.e., curing regime) has the most influence compared to FA, NaOH, and CAg then the other inputs. From the model relations between the calculated and predicted values, it can be shown that the decisive model, ANN produced line of parametric equation of y = 0.995x, and produced performance indices; MAE of 2.13 MPa, RMSE of 2.86 MPa and R-squared of 0.981, which makes the ANN the most reliable model in agreement with previous applications of the technique. These are against the poor performance of the EPR and GP, which produced R-squared less than 0.8 with higher error rates. The Taylor chart and the variance distribution, which further compares the accuracy and variances of the developed models support the outcomes. Generally, alkali molarity has shown its potential in the production of HSGPC due to its role in the reactivity phases of the concrete formulation; hydration, activation, pozzolanic, and geopolymerization reactions producing the gel needed for the strength gain in HSGPC.