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

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