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This mini-review examines the intersection of corrosion science and artificial intelligence (AI), focusing on how AI technologies enhance the detection, monitoring, and mitigation of corrosion in industrial systems. It explores recent advancements in AI-driven methodologies, such as machine learning (ML), deep learning (DL), and data analytics, which improve corrosion modeling, prediction, and real-time monitoring. These advancements offer promising avenues for maintaining infrastructure integrity, reducing economic losses, and minimizing environmental impacts. By integrating AI with traditional corrosion management practices, industries can achieve more accurate, efficient, and cost-effective solutions, enhancing safety and sustainability in critical sectors. This review also discusses future prospects, including hybrid models, autonomous systems, and AI-driven materials, highlighting the potential for AI to revolutionize corrosion prevention and contribute to the broader goals of sustainable industrial development.

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Leveraging Artificial Intelligence for Corrosion Detection and Prevention: A Review of Current Trends and Future Prospects

  • 会议论文

  • 发表日期：

    2025年

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  • Ichraq Bouhouche,
  • Najoua Labjar,
  • Mohamed Omari,
  • Abdellatif Aarfane,
  • Abdoullatif Baraket,
  • Hamid Nasrellah,
  • Meryem Bensemlali,
  • Houda Labjar,
  • Mohamed Kissi,
  • Souad El Hajjaji

摘要

This mini-review examines the intersection of corrosion science and artificial intelligence (AI), focusing on how AI technologies enhance the detection, monitoring, and mitigation of corrosion in industrial systems. It explores recent advancements in AI-driven methodologies, such as machine learning (ML), deep learning (DL), and data analytics, which improve corrosion modeling, prediction, and real-time monitoring. These advancements offer promising avenues for maintaining infrastructure integrity, reducing economic losses, and minimizing environmental impacts. By integrating AI with traditional corrosion management practices, industries can achieve more accurate, efficient, and cost-effective solutions, enhancing safety and sustainability in critical sectors. This review also discusses future prospects, including hybrid models, autonomous systems, and AI-driven materials, highlighting the potential for AI to revolutionize corrosion prevention and contribute to the broader goals of sustainable industrial development.