海底管道
腐蚀
海洋工程
海上油气
石油工程
工程类
计算机科学
机器学习
算法
人工智能
材料科学
冶金
岩土工程
作者
Md Mahadi Hasan Imran,Shahrizan Jamaludin,Ahmad Faisal Mohamad Ayob
标识
DOI:10.1016/j.oceaneng.2024.116796
摘要
Corrosion presents significant challenges in the marine, offshore, and oil and gas industries, resulting in annual economic losses amounting to billions of dollars. To address these losses and ensure the structural integrity of marine infrastructure, it is essential to implement effective corrosion monitoring techniques. In recent years, machine learning algorithms have gained prominence across various fields, offering innovative solutions to corrosion-related concerns. This paper provides a comprehensive critical review, primarily focusing on the two most prevalent machine learning algorithms: artificial neural networks and random forests. The review critically analyzes their applications, methodologies, and effectiveness in the realm of marine and offshore steel structures, oil and gas pipelines, as well as construction materials like Al alloys and Mg alloys and the analysis of corrosion coating behavior. Furthermore, this study explores the key findings and inherent limitations of these machine learning techniques, emphasizing their potential in corrosion prediction, detection, and the mitigation of corrosion issues in the marine, offshore, and oil and gas industries. By identifying existing research gaps and offering recommendations for future investigations, this paper emerges as an invaluable resource for researchers, engineers, and practitioners aiming to advance corrosion prevention and management strategies within these pivotal domains.
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