The numerical modeling of fiber reinforced concrete (FRC) structures is quite challenging due to the material's heterogeneous and anisotropic nature. The majority of material models that are suitable for regular concrete are not able to account for the FRC material’s increased tensile capacity and ductility. In this study, a calibration method is proposed that is simple and effective for modeling FRC structures using a selected concrete material model. The Karagozian and Case (K&C) material model in LS-DYNA is capable of representing the ductile nature of FRC, and its parameters related to tensile behavior were calibrated to reflect the tensile-softening behavior. The calibration process was executed using the uniaxial direct tension test results of two different FRC mixtures. In addition, single element numerical models were constructed using LS-DYNA under uniaxial tension. The tensile parameters of K&C were varied over a wide range using single elements to form a database. Then, a Deep Neural Network (DNN) was constructed to pass the database through and find the K&C parameters that best matched the experimental uniaxial test results. The proposed methodology was tested under static and high-strain rate loading conditions, and the results were compared to the experimental findings. The performance of the DNN-achieved parameters was found to be satisfactory. The results showed that the DNN-calibrated parameters were able to accurately predict the behavior of FRC structures under static and dynamic loading conditions.