Predicting compressive strength of concrete with fly ash, metakaolin and silica fume by using machine learning techniques



The compressive strength (CS) is the most important parameter in the design codes of reinforced concrete structures. The development of simple mathematical equations for the prediction of CS of concrete can have many practical advantages such as it save cost and time in experiments needed for suitable design data. Due to environmental concerns with the production of cement, different supplementary cementitious materials are often used as partial replacements for cement such as fly ash (FA), metakaolin (MK), and silica fume (SF). However, little work has been done for developing simple mathematical equations for the prediction of CS with FA, MK and SF by using the M5P algorithm. Moreover, the M5P algorithm is not compared with other modelling techniques such as linear regression analysis, gene expression programming (GEP) and response surface methodology. It is established that, for concrete with FA and SF, M5P showed superior prediction capability as compared with other modelling techniques, however, GEP gave the best performance for concrete with MK: CS decrease by increasing FA content, while it increases by increasing MK and SF content.