Data-driven modeling for residual velocity of projectile penetrating reinforced concrete slabs

Abstract

The penetration of projectiles into reinforced concrete (RC) structures is crucial for defense technology. However, accurately estimating the resistance of RC structures to projectile penetration is challenging due to complex parameter nonlinearities. This paper proposes a data-driven modeling methodology that combines validated finite element (FE) simulations with machine learning (ML) algorithms to investigate the penetration resistance of RC slabs against projectiles. The methodology employs an extreme learning machine (ELM) network to identify extreme conditions characterizing penetration resistance, reducing data requirements, and improving prediction efficiency. Predicted results from the ELM network are then used as input for a recurrent neural network (RNN) to predict residual velocity curves over time, a crucial metric for evaluating penetration resistance. To cover various loading scenarios, 64 sets of FE models with different incident velocities, caliber-radius-head (CRH), and projectile masses are developed based on available experimental studies. The Johnson-Holmquist II model and Johnson-Cook model are employed for concrete and steel, respectively, to describe material behavior at high strain rates. The FE prediction results form a training sample database, and ML models are established using CRH, incident velocity, and projectile mass as input layers and projectile residual velocity as the output layer. The results demonstrate high Pearson correlation coefficients of 0.984 and 0.991 for the ELM and RNN prediction results, respectively, with numerical simulation results. The proposed networks provide accurate and efficient alternatives to time-consuming FE simulations for predicting projectile penetration performance. This strategy offers a new ML-based method for rapid evaluation, particularly for in-situ applications.