Genetic Programming Based Formulation to Predict Compressive Strength of High Strength Concrete

Abstract

This study introduces, two models based on Gene Expression Programming (GEP) to predict compressive strength of high strength concrete (HSC). Composition of HSC was assumed simplified, as a mixture of six components (cement, silica fume, super-plastisizer, water, fine aggregate and coarse aggregate). The 28-day compressive strength value was considered the target of the prediction. Data on 159 mixes were taken from various publications. The system was trained based on 80% training pairs chosen randomly from the data set and then tested using remaining 20% samples. Therefore it can be proven and illustrated that the GEP is a strong technique for the prediction of compressive strength amounts of HSC concerning to the outcomes of the training and testing phases compared with experimental outcomes illustrate that the.