Developing Artificial Neural Networks Model for Concrete Mix Design

Analyzing concrete samples in the laboratory necessitates costly and time-consuming experiments. Advancements in artificial intelligence provide researchers with a helpful tool for extracting information regarding experimental and physical property relationships in a more sophisticated manner to predict concrete mix properties. In this inquiry, ninety concrete mix experiment samples are utilized. This study aims to predict concrete mix qualities, namely compressive strength. Several amounts of silica fume addition, milling duration, and water content ratio were planned into 90 concrete blocks for use in laboratory research. We measure compressive strength which is major concrete property after 28 days. Using five input variables, an ANN model was trained to forecast the concrete compressive strength. The trained ANN model show a correlation value of 0.98, which is quite high. The created ANN model is a useful tool for prediction of concrete mix behavior.