The study discusses an application of the physics-aware symbolic regression (PASR) paradigm, with the embedded Recht–Ipson (R-I) model, to describe the relationship between the 7.62mm AP P80 projectile’s initial impact and the residual velocities of the S355 steel plates of varying
thicknesses. The present approach results in a straightforward analytical “SR R-I” formula, which can be easily applied in engineering practice to determine ballistic limit curves and velocities (BLCs and BLVs). The proposed approach has demonstrated statistical effectiveness that outperforms other machine learning (ML) algorithms in both fitting accuracy and the predictive capability of these terminal ballistic features.
REFERENCES(30)
1.
Abid, K.A., Syed, S.A., & Khan, M. (2026). Interpretable machine learning models for predicting penetration depth in ultra-high-performance concrete under ballistic impact. Structural Concrete. https://doi.org/10.1002/suco.7....
Anderson Jr., C.E., & Bodner, S.R. (1988). Ballistic impact: The status of analytical and numerical modeling. International Journal of Impact Engineering, 7 (1), 9–35. https://doi.org/10.1016/0734-7....
Deng, Y., Lv, Y., Yang, X., Du, C., & Huang, X. (2025). Determination of residual velocity model of finite thickness material based on Artificial Neural Network. Thin-Walled Structures, 216 (Part B), Article 113685. https://doi.org/10.1016/j.tws.....
El Hasadi, Y.M.F., & Elghannay, H.A. (2026). A physics aware symbolic regression formula for the wall effects on a sphere moving inside a cylindrical tube. International Journal of Multiphase Flow, 198, Article 105640. https://doi.org/10.1016/j.ijmu....
European Committee for Standardization. (2019). Hot rolled products of structural steels - Technical delivery conditions for non-alloy structural steels (EN 10025-2:2019).
Fonseca, L.C., Peixoto, F.C., & Nichele, J. (2025). Yaw-induced transitions in ballistic limit and fracture mechanisms in steel plate perforation (version 1). Research Square. https://doi.org/10.21203/rs.3.....
Fras, T., & Pawlowski, P. (2026). On the applicability of generative artificial intelligence in modelling the dynamic behaviour of materials–potentials and pitfalls. Journal of Dynamic Behavior of Materials. https://doi.org/10.1007/s40870....
Frąś, T., Nowak, Z., Perzyna, P., & Pęcherski, R.B. (2011). Identification of the model describing viscoplastic behaviour of high strength metals. Inverse Problems in Science and Engineering, 19 (1), 17–30. https://doi.org/10.1080/174159....
Hachaj, T., & Frąś, T. (2026). Approximation of two-dimensional ballistic limit curves in the range of material thickness with stochastic physical-based oversampling. Bulletin of the Polish Academy of Sciences Technical Sciences, 74 (3), Article e158297. https://doi.org/10.24425/bpast....
Li, W., Bian, Z., Chen, T., Mao, C., & Zhang, C. (2026). Reverse prediction of impact parameters for 3D woven fabrics subjected to ballistic impact using deep learning methods. Mechanics of Advanced Materials and Structures, 33 (1), Article 2569075. https://doi.org/10.1080/153764....
Rietkerk, R., Heine, A., & Riedel, W. (2023). Physics-informed machine learning model for prediction of long-rod penetration depth in a semi-infinite target. International Journal of Impact Engineering, 173, Article 104465. https://doi.org/10.1016/j.ijim....
Shen, J., Tenenholtz, N., Hall, J.B., Alvarez-Melis, D., & Fusi, N. (2024). Tag-LLM: Repurposing general-purpose LLMs for specialized domains. Proceedings of the 41st International Conference on Machine Learning, 235, 44759–44773.
Taskin, B., Xie, W., & Lazebnik, T. (2026). Knowledge integration for physics-informed symbolic regression using pre-trained large language models. Scientific Reports, 16, Article 1614. https://doi.org/10.1038/s41598....
Zou, D., Thoti, D., & Bao, Z. (2025). Study on the application of discrepancy-guided symbolic regression algorithm in analyzing the impact resistance of UHP-SFRC target against high velocity projectile impact. International Journal of Impact Engineering, 201, Article 105276. https://doi.org/10.1016/j.ijim....
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.