Ballistic impact: predicting penetration depth in ultra-high-performance concrete targets

Abstract

This paper presents the application of an artificial neural network (ANN) model for predicting the penetration depth under projectile impact in ultra-high-performance concrete (UHPC) targets containing steel fibres. Despite the availability of a large number of existing empirical models, the prediction of penetration depth remained inconclusive, partly due to the phenomenon's complexity and partly due to the limitation of statistical regression. From the results of this study, it is evident that the ANN model is capable of predicting the penetration depth of UHPC more accurately than other machine-learning (ML) models (linear regression, decision tree regression and random forest regression) and empirical formulae. The ANN model achieved a lower root mean square error (RMSE) of 11.68 compared to the other ML models (RMSE: 16.66–19.74) and empirical equations (RMSE: 25.17–53.42), when applied to the test data set. The velocity, impact energy, diameter of the projectile and thickness of the UHPC targets are the most significant parameters (P-value <5%) for predicting the penetration depth using ANN and multiple linear regression models.