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Abstract
This study involves a three-phase experimental and analytical investigation of the mechanical performance enhancement potentials of steel fiber reinforced concrete (SFRC). In phase I, thirteen mixtures were batched to study the fresh and hardened properties of SFRC containing varying fiber geometries and concentrations. Based on results of investigative mixtures’ fresh properties, compressive strength, and modulus of rupture, mixtures were selected for Phase II large-scale static and impact beam testing. Phase II involved testing of large-scale SFRC beams containing differing levels of shear and flexural reinforcement to quantify the additional shear and flexural capacity provided by steel fibers. Within phase III, machine learning methods were used to construct SFRC compressive and flexural strength prediction models. From this study, SFRC was analyzed for potential use in GDOT applications with an understanding of the influence fiber reinforcement has on concrete fresh and hardened properties.