Modeling the strength parameters of agro waste-derived geopolymer concrete using advanced machine intelligence techniques

Abstract The mechanical strength of geopolymer concrete incorporating corncob ash and slag (SCA-GPC) was estimated by means of three distinct AI methods: a support vector machine (SVM), two ensemble methods called bagging regressor (BR), and random forest regressor (RFR). The developed models were validated using statistical tests, absolute error assessment, and the coefficient of determination ( R 2 ). The importance of various modeling factors was determined by means of interaction diagrams. When estimating the flexural strength and compressive strength of SCA-GPC, R 2 values of over 0.85 were measured between the actual and predicted findings using both individual and ensemble AI models. Statistical testing and k -fold analysis for error evaluation revealed that the RFR model outperformed the SVM and BR models in terms of accuracy. As demonstrated by the interaction graphs, the mechanical characteristics of SCA-GPC were found to be extremely responsive to the mix proportions of ground granulated blast furnace slag, fine aggregate, and corncob ash. This was the case for all three components. This study demonstrated that highly precise estimations of mechanical properties for SCA-GPC can be made using ensemble AI techniques. Improvements in geopolymer concrete performance can be achieved by the implementation of such practices.