Paper Title
Prediction Of Compressive Strength Of Fiber Reinforced Concrete Using Artificial Neural Networks

Abstract
Fiber reinforced concrete (FRC) is a type of concrete that contains discontinuous fibers distributes randomly among the concrete block. In this paper, the Artificial Neural Networks are utilized to predict the effect of the addition of steel nails as fibers on the compressive strength of concrete. The study involves testing of cubic concrete samples with various mixing proportions and water cement ratios. The results showed that (for mixing proportion 1:1.5:3) the compressive strength has the more increasing when the fibers are added with 12%, while it has the more increasing at 20% fibers adding for mixing proportion (1:2:4). It is also found that the optimum water cement ratio is found to be 46% for the mixing proportion (1:1.5:3) with 12% fibers and 55% for mixing of (1:2:4) with fibers adding 20%. The results showed also that the increasing of the percentage of fibers added with mixing ratio (1:1.5:3) leads the compressive strength to increase more uniformly and effectively than the use of the mixing ratio (1:2:4). Also it is found that using a larger size of nails with low percent of addition will significantly increase the compressive strength with the increasing of percentage of addition the compression strength decreases. Indexterms- Fiber Reinforced Concrete, Prediction of Compressive Strength, Neural Net Works, Reinforced Concrete