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The mechanical properties of concrete are one of the most important properties in a design code. Accurate prediction models for mechanical properties are always desirable. Given environmental benefits, fly ash (FA) is often used as a replacement for Portland cement. In this study, two mechanical properties of concrete with FA, that is, compressive strength f 0 c ð Þ and splitting tensile strength f st ð Þ were modeled by using gene expression programming (GEP).The GEP models were compared with response surface methodology, multiple linear and nonlinear regression. The sensitivity and parametric analysis were also performed. In addition, the influence of parameters of the GEP algorithm on the performance of the developed GEP models was evaluated by testing various linking functions, head sizes, number of genes, number of chromosomes, and fitness functions. It was revealed that the models developed by GEP have high predictive and generalization capability for both f 0 c and f st of concrete with FA as compared with the other modeling techniques.

The mechanical properties of concrete are the important parameters in a design code. The amount of laboratory trial batches and experiments required to produce useful design data can be decreased by using robust prediction models for the mechanical properties of concrete, which can save time and money. Portland cement is frequently substituted with metakaolin (MK) because of its technical and environmental advantages. In this study, three mechanical properties of concrete with MK, i.e., compressive strength (f′c), splitting tensile strength (fst), and flexural strength (FS) were modelled by using four machine learning (ML) techniques: gene expression programming (GEP), artificial neural network (ANN), M5P model tree algorithm, and random forest (RF). For this purpose, a comprehensive database containing detail of concrete mixture proportions and values of f′c, fst, and FS at different ages was gathered from peer-reviewed published documents. Various statistical metrics were used to compare the predictive and generalization capability of the ML techniques. The comparative study of ML techniques revealed that RF has better predictive and generalization capability as compared with GEP, ANN, and M5P model tree algorithm. Moreover, the sensitivity and parametric analysis (PA) was carried out. The PA showed that the most suitable proportions of MK as partial cement replacement were 10% for FS and 15% for both f′c and fst.