Optimization Framework for Ammunition Damage Effectiveness Evaluation Based on Artificial Intelligence

YANG Weitao; LIU Kangtai; DENG Liu

doi:10.7643/ issn.1672-9242.2025.09.011

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Equipment Environmental Engineering ›› 2025, Vol. 22 ›› Issue (9) : 94-104. DOI: 10.7643/ issn.1672-9242.2025.09.011

Weapons Equipment

Optimization Framework for Ammunition Damage Effectiveness Evaluation Based on Artificial Intelligence

YANG Weitao¹, LIU Kangtai¹, DENG Liu^2,*

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Abstract

To address the limitations of traditional ammunition damage effectiveness evaluation methods on modern battlefields, the work aims to propose an intelligent evaluation system based on artificial intelligence (AI) technology to solve issues such as low efficiency, insufficient accuracy, and poor real-time performance. Firstly, the limitations of traditional methods, including field tests, theoretical models, and empirical formulas were analyzed and it was pointed out that these methods were inadequate for meeting the demands of modern warfare, such as complex targets, diverse scenarios, and dynamic environments. To tackle these challenges, core AI technologies, including machine learning, deep learning, and reinforcement learning, were introduced to build a data-driven assessment system aimed at improving prediction accuracy and real-time performance through intelligent modeling and automation. The system architecture of the intelligent evaluation platform was also elaborated, covering modules for data acquisition, multi-source data fusion, AI modeling, and feedback of results, emphasizing the generalizability and adaptability of the model to real-world combat scenarios. Furthermore, future research directions were discussed, including multi-modal data fusion, small-sample learning, virtual-real integration, and trustworthy AI. The study demonstrates that AI-based ammunition damage effectiveness evaluation provides innovative theoretical and technical support for the precise evaluation of modern weapon systems, driving the development of evaluation methods toward intelligence and efficiency, with significant military application prospects.

Key words

ammunition / damage effectiveness / artificial intelligence / machine learning / evaluation method / data fusion

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YANG Weitao, LIU Kangtai, DENG Liu. Optimization Framework for Ammunition Damage Effectiveness Evaluation Based on Artificial Intelligence[J]. Equipment Environmental Engineering. 2025, 22(9): 94-104 https://doi.org/10.7643/ issn.1672-9242.2025.09.011

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