Assessment of flexural and splitting strength of fiber-reinforced concrete using artificial intelligence

Flexural and splitting strength behavior of conventional concrete can be significantly improved by incorporating fibers into it. A significant number of research studies have been conducted on various types of fibers and their influence on the tensile capacity of concrete. However, as an important property, tensile capacity of fiber-reinforced concrete (FRC) is not modeled properly. Therefore, this article intends to formulate an artificial neural network (ANN) model based on experiments that show the relationship between the fiber properties such as the aspect ratio (length/diameter), fiber content, compressive strength, flexural strength, and splitting strength of FRC. For ANN modeling, various FRC mixes with only steel fiber are adopted from the existing research papers. An artificial intelligence approach such as artificial neural network (ANN) is developed and used to investigate the effect of input parameters such as fiber content, aspect ratio, and compressive strength to the output parameters of flexural and splitting strength of FRC. It is found that the ANN model can be used to predict the flexural and splitting strength of FRC with sensible precision.