Utilization of GEP and ANN for predicting the net-zero compressive strength of fly ash concrete toward carbon neutrality infrastructure regime

Abstract

Abstract The present infrastructure regime being promoted by the United Nations Sustainable Development Goals is such that by the year 2050, the use of cement in the production of concrete and its use in the general construction activities as to reduce carbon emissions to zero must be replaced with net-zero construction materials. These cement replacement materials should be pozzolanic enough to either partially or totally replace the conventional cement and reduce its carbon footprint. The current study adopts two machine learning techniques: gene expression programming (GEP) and artificial neural network (ANN) to determine the 56 days and 180 days of net-zero compressive strength of fly ash concrete. The study effectively depicts how machine learning techniques can be used for the prediction of long- and short-term compressive strength of fly ash concrete toward a carbon neutrality infrastructure regime. The dataset has been compiled by various researchers, and the input parameters include cement, fine aggregate, coarse aggregate, fly ash, water, and water/binder ratio. And the 56 days and 180 days compressive strength (fck) values are the targeted output values. In order to determine a better model, both GEP and ANN were assessed based on the values of the correlation coefficient and crosschecked by other statistical parameters. Both models performed well; however, GEP outweighs the ANN model in estimating the fck at 56 days and 180 days. Moreover, the GEP model generated a simplified equation for foreseeing the value of fck for different ages of net-zero fly ash concrete.