Concrete is a strain-rate sensitive material and shows relatively low ductility and energy dissipation capacity under high strain rate loads (HSRL) such as blast and impact, representative of terrorist attacks and accidents. Experimental research in the literature has evidenced that introducing steel fibers, into the concrete mixtures can significantly improve the concrete behavior under HSRL. Besides the experimental research, development of design models is an important aspect to provide more confidence for engineers to use SFRC in structural elements when subjected to HSRL. The existing design codes (e.g. CEB-FIP Model Code 1990 and fib Model Code 2010) propose models for the prediction of the strengths of concrete under different HSRL, but they are only function of strain rate. In this regard, the current paper deals with the improvement of design models in the fib Model Code 2010 for the prediction of the compressive behavior of SFRC by considering the effects of the important parameters such as volume fraction, aspect ratio and tensile strength of steel fibers, and concrete compressive strength, besides the strain rate effect. The developed artificial neural network mathematical model is calibrated and its predictive performance is assessed using a database collected from the existing compressive impact tests results on SFRC specimens.