Neural Network Approach for Estimation of Penetration Depth in Concrete Targets by Ogive-nose Steel Projectiles

Despite the availability of large number of empirical and semi-empirical models, the problem of penetration depth prediction for concrete targets has remained inconclusive partly due to the complexity of the phenomenon involved and partly because of the limitations of the statistical regression employed. Conventional statistical analysis is now being replaced in many fields by the alternative approach of neural networks. Neural networks have advantages over statistical models like their data-driven nature, model-free form of predictions, and tolerance to data errors. The objective of this study is to reanalyze the data for the prediction of penetration depth by employing the technique of neural networks with a view towards seeing if better predictions are possible. The data used in the analysis pertains to the ogive-nose steel projectiles on concrete targets and the neural network models result in very low errors and high correlation coefficients as compared to the regression based models.