BACK PROPAGATION ARTIFICIAL NEURAL NETWORK MODELING OF FLEXURAL AND COMPRESSIVE STRENGTH OF CONCRETE REINFORCED WITH POLYPROPYLENE FIBERS

Abstract

The production of fiber reinforced concrete involves a complex reaction system. This imposes an
immense challenge in deriving appropriate material proportions of concrete to achieve desired mechanical
properties. In order to facilitate selection of a design matrix for fiber reinforced concrete, a novel artificial Neural
Network models for compressive and flexural strengths using back propagation feed-forward algorithm were
proposed in this research. A wide range of varied concrete design mixes of cylindrical and beam samples was
respectively tested for compressive and flexural tests. A polypropylene type of fiber reinforcement was used in the
preparation of samples that were cured for 28 days in a water-saturated lime. Results showed that the compressive
and flexural strength models provided predictions in good agreement with experimental results as described by
high correlation values of 99.46% and 98.57% respectively. Mean squared errors of 0.0024 and 0.44 were obtained
respectively in selecting the best fit model for compressive and flexural strengths. In the parametric analysis
conducted, the proposed models were able to describe analytically the constitutive relationships of the material
components and capture the dominant characteristics of concrete samples.