Scholarlink

<p>This systematic review explores research advancements in the field of machine learning-assisted design of reinforced composite radiation shields. The goal is to break down ongoing progressions and accomplishments in optimizing shielding materials through the integration of machine learning (ML) strategies. A precise search was directed across conspicuous data bases, including PubMed, IEEE Xplore, Google Scholar, and Scopus, from 2020 to 2024. Consideration models enveloped examinations zeroed in on the utilization of ML in the design and optimization of built-up composite radiation shields. A total of 194 studies meeting the eligibility criteria were included for detailed analysis. Study selection was based on predefined criteria, considering relevance to the integration of ML in the design of composite shields. Data extraction involved key parameters such as material composition, geometric design, and processing techniques used in the optimization process. Research advancements identified in the ML-assisted design of reinforced composite radiation shields include precise tailoring of material composition for enhanced radiation absorption, geometric optimization strategies for improved shielding efficacy, and successful applications across diverse industries such as healthcare, aerospace, and nuclear technology. This systematic review highlights the transformative impact of ML on the design of reinforced composite radiation shields. Recent achievements indicate a clear understanding of material interactions, leading to the creation of highly efficient shielding materials. The integration of computational tools and experimental validation has opened new frontiers in safety, efficiency, and innovation. Future research directions should focus on interpretability, addressing challenges in data availability, and ensuring ethical considerations in the design process.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Research advancements in machine learning-assisted design of reinforced composite radiation shields

  • 期刊论文

  • 发表日期：

    2025年4月29日

查看全文
  • Ugochukwu Chuka Okonkwo,
  • Christian Emeka Okafor,
  • Shahnawaz Ahmad,
  • Chukwuemeka Christopher Ohagwu,
  • Michael Echeta Aronu,
  • Imhade Princess Okokpujie,
  • Christopher Igwe Idumah,
  • Nelson Nnaemeka Chukwu,
  • Christian Ebele Chukwunyelu,
  • Jiyaul Mustafa

摘要

This systematic review explores research advancements in the field of machine learning-assisted design of reinforced composite radiation shields. The goal is to break down ongoing progressions and accomplishments in optimizing shielding materials through the integration of machine learning (ML) strategies. A precise search was directed across conspicuous data bases, including PubMed, IEEE Xplore, Google Scholar, and Scopus, from 2020 to 2024. Consideration models enveloped examinations zeroed in on the utilization of ML in the design and optimization of built-up composite radiation shields. A total of 194 studies meeting the eligibility criteria were included for detailed analysis. Study selection was based on predefined criteria, considering relevance to the integration of ML in the design of composite shields. Data extraction involved key parameters such as material composition, geometric design, and processing techniques used in the optimization process. Research advancements identified in the ML-assisted design of reinforced composite radiation shields include precise tailoring of material composition for enhanced radiation absorption, geometric optimization strategies for improved shielding efficacy, and successful applications across diverse industries such as healthcare, aerospace, and nuclear technology. This systematic review highlights the transformative impact of ML on the design of reinforced composite radiation shields. Recent achievements indicate a clear understanding of material interactions, leading to the creation of highly efficient shielding materials. The integration of computational tools and experimental validation has opened new frontiers in safety, efficiency, and innovation. Future research directions should focus on interpretability, addressing challenges in data availability, and ensuring ethical considerations in the design process.