Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic

Abstract

In this study, artificial neural networks and fuzzy logic models for predicting the 7, 28 and 90 days compressive strength of concretes containing high-lime and low-lime fly ashes have been developed. For purpose of constructing these models, 52 different mixes with 180 specimens were gathered from the literature. The data used in the artificial neural networks and fuzzy logic models are arranged in a format of nine input parameters that cover the day, Portland cement, water, sand, crushed stone I (4–8 mm), crushed stone II (8–16 mm), high range water reducing agent replacement ratio, fly ash replacement ratio and CaO, and an output parameter which is compressive strength of concrete. In the models of the training and testing results have shown that artificial neural networks and fuzzy logic systems have strong potential for predicting 7, 28 and 90 days compressive strength of concretes containing fly ash.