The quintenary influence of industrial wastes on the compressive strength of high-strength geopolymer concrete under different curing regimes for sustainable structures; a GSVR-XGBoost hybrid model

The production of geopolymer concrete (GPC) with the addition of industrial wastes as the formulation base is of interest to sustainable built environment. However, repeated experimental trials costs a huge budget, hence the prediction and validation of the strength behavior of the GPC mixed with some selected industrial wastes. Data gathering and analysis of a total 249 globally representative datasets of a high-strength geopolymer concrete (HSGPC) collected from experimental mix entries has been used in this research work. These mixes comprised of industrial wastes; fly ash (FA) and metallurgical slag (MS) and mix entry parameters like rest period (RP), curing temperature (CT), alkali ratio (AR), which stands for NaOH/Na 2 SiO 3 ratio, superplasticizer (SP), extra water added (EWA), which was needed to complete hydration reaction, alkali molarity (M), alkali activator/binder ratio (A/B), coarse aggregate (CAgg), and fine aggregate (FAgg). These parameters were deployed as the inputs to the modeling of the compressive strength (CS). The range of CS considered in this global database was between 18 MPa and 89.6 MPa. The FA was applied between 254.54 kg/m 3 and 515 kg/m 3 while the MS was applied between 0% and 100% by weight of the FA to produce the tested HSGPC mixes. The Gaussian support vector regression hybridized with the extreme gradient boosting algorithms (GSVR-XGB) has been deployed to execute a prediction model for the studied concrete CS. The basic linear fittings to determine agreement between the parameters and the Pearson correlation between the studied parameters of the geopolymer concrete were presented. It can be observed that the CS showed very poor correlations with the values of the input parameters and required an improvement of the internal consistency of the dataset to achieve a good model performance. This necessitated the deployment of the super-hybrid interface between the Gaussian support vector regression (GSVR) and the extreme gradient boosting (XGB) algorithms. The frequency histogram and the Gaussian support vector machine architecture for the output (CS) are presented and these show serious outliers in the support vector machine which were tuned by using the boosting algorithms combined in the computation interface to enhance the GSVR hyperplane. This eventually produced a super-performance and execution speed remarkable for its use in the forecasting of the CS of the high-strength geopolymer concrete (HSGPC) for sustainable