Prediction model for projectile ballistic characteristics in multi-layered spaced concrete thin targets based on CNN

LIANG Junxuan; MA Luyao; LIU Chuang; SHEN Taoran; ZHAI Zhe; XIAO Chuan; ZHANG Xianfeng

doi:10.11883/bzycj-2025-0320

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LIANG Junxuan, MA Luyao, LIU Chuang, SHEN Taoran, ZHAI Zhe, XIAO Chuan, ZHANG Xianfeng. Prediction model for projectile ballistic characteristics in multi-layered spaced concrete thin targets based on CNN[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0320

Citation:

LIANG Junxuan, MA Luyao, LIU Chuang, SHEN Taoran, ZHAI Zhe, XIAO Chuan, ZHANG Xianfeng. Prediction model for projectile ballistic characteristics in multi-layered spaced concrete thin targets based on CNN[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0320

Citation:

LIANG Junxuan, MA Luyao, LIU Chuang, SHEN Taoran, ZHAI Zhe, XIAO Chuan, ZHANG Xianfeng. Prediction model for projectile ballistic characteristics in multi-layered spaced concrete thin targets based on CNN[J]. Explosion And Shock Waves. doi: 10.11883/bzycj-2025-0320

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Prediction model for projectile ballistic characteristics in multi-layered spaced concrete thin targets based on CNN

doi: 10.11883/bzycj-2025-0320

1.
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, Jiangsu, China
2.
China North Industries Corp., Beijing 100053, China

Received Date: 2025-09-28
Rev Recd Date: 2026-01-06

Available Online: 2026-01-08

Abstract

Abstract

To overcome the high computational cost of traditional ballistic prediction methods and their inability to satisfy rapid assessment requirements, this study proposes an efficient predictive model for the penetration ballistics of multi-layer thin concrete targets based on a Convolutional Neural Network (CNN). First, a numerically simulated approach, validated by experiments, was employed to analyze and confirm the significant influence of projectile angular velocity on trajectory deflection, and this parameter was consequently identified as a key projectile–target engagement condition. By systematically varying the initial conditions, a dataset comprising 127 cases of single-layer thin concrete target penetration was constructed. On this basis, a high-accuracy ballistic prediction model for single-layer targets was developed, taking projectile parameters, target parameters, and engagement conditions as inputs, and post-impact projectile motion parameters as outputs. Furthermore, by incorporating rigid-body kinematic equations describing the projectile flight between successive targets, a complete iterative penetration–flight prediction framework was established, enabling rapid prediction of ballistic characteristics for multi-layer spaced thin concrete targets. The results indicate that an increase in counterclockwise angular velocity leads to a positive increase in the radial residual velocity behind the target and an upward deflection of the trajectory, whereas clockwise angular velocity produces the opposite effect. These findings demonstrate that projectile angular velocity is a critical and non-negligible factor in thin-target penetration. For single-layer target cases, the model exhibited strong predictive capability, with the mean MSE values of the training and test sets stabilizing at approximately 0.0012 and 0.0019, respectively. For multi-layer target predictions, while maintaining high accuracy (with a maximum relative error of 10.65% in residual velocity and a maximum absolute error of 3.47° in attitude angle), the computational time of the proposed method was only about 0.05% of that required by conventional numerical simulation. This study not only elucidates the influence of the key factor-projectile angular velocity-on penetration ballistics, but also proposes a novel “data-driven and physics-equation fusion” modeling paradigm, providing an important methodological reference for weapon damage effectiveness assessment and design optimization.
- penetrating projectile,
- multi-layered spaced concrete,
- ballistic characteristics,
- convolutional neural network,
- prediction model

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References(25)

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