A critical review of machine learning algorithms in maritime, offshore, and oil & gas corrosion research: A comprehensive analysis of ANN and RF models

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.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2024.116796
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801824001331
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2024 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Imran, Md Mahadi Hasan
  • Jamaludin, Shahrizan
  • 0000-0002-9523-2415
  • Ayob, Ahmad Faisal Mohamad
• Publication Date: 2024-3-1

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 116796
• Serial:
  • Ocean Engineering
  • Volume: 295
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Algorithms; Corrosion; Decision trees; Machine learning; Neural networks; Offshore oil industry; Offshore structures
• Subject Areas: Maintenance and Preservation; Marine Transportation; Terminals and Facilities;

Filing Info

• Accession Number: 01908289
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 15 2024 5:04PM