In the present study, numerical analysis was performed to predict the amount of concrete fragments generated and the distance travelled by the fragments under impact loading using Smooth Particle Hydrodynamics (SPH). SPH can be used for predicting the amount of fragmentation or the motion of fragmentations. The obtained results of the SPH analysis showed that the amount of fragments and the travel distance can change depending on different velocity-to-mass ratios under same local impact energy. Using the results of the SPH analysis, artificial neural network (ANN) was constructed to consider the uncertainty from the prediction of the fragmentations and travel distance. Furthermore, the results of ANN were compared with the results of Multiple Linear Regression Analysis (MRA). The ANN results showed better correlation coefficient (R2) than the MRA results. Therefore, ANN showed better improvement with consideration of the uncertainty from the prediction of fragmentations and travel distance than the MRA results. Using the constructed ANN, data augmentation was conducted from a limited number of actual data using a statistical distribution method. Finally, the fragility curves of the concrete median barrier were obtained to estimate the probability of occurrence of specific fragmentation amount and travel distance under same impact energy.