A bibliometric analysis on the relevancies of artificial neural networks (ANN) techniques in offshore engineering

Muhammad Daniel Abdul Shahid, Mohd Hisbany Mohd Hashim*, Najwa Mohd Fadzil, Muhd Hariz Ahmad Rushdi, Amin Al-Fakih, Mohd Fakri Muda

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review


Recently, the use of artificial neural networks (ANN) in the offshore exploration and production industry to optimize decision-making and reduce costs and non-productive time have been increasing. Despite this trend, there have been only 11 reviews of ANN in offshore engineering published on the Scopus database. Therefore, this article aims to provide an update on the relevance of ANN in offshore engineering over the past 18 years (2005–2023) through a bibliometric analysis using Excel and VOS Viewer software. This analysis highlights the yearly increase in publications related to ANN implementations in offshore engineering and identifies the most cited publications, citation network analysis, authors, keywords, journals, institutions, and leading countries. The objective of this bibliometric analysis is to assist subsequent research and collaboration in this field by shedding light on ANN’s potential and identifying areas for further application. The identified cluster area publications encompass a range of topics, including drilling systems and the assessment of pipes. Furthermore, the significant fourfold increase in publications since 2005 indicates a growing interest among researchers in adapting ANN for various applications within this field. This could lead to further advancements, innovations, and improved solutions to promote collaboration and knowledge-sharing among researchers in this domain.

Original languageEnglish
Article number2241729
JournalCogent Engineering
Issue number1
StatePublished - 2023

Bibliographical note

Funding Information:
The authors would like to thank the School of Civil Engineering, College of Engineering, Universiti Teknologi MARA (UiTM) for supporting this research.

Publisher Copyright:
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.


  • VOS Viewer
  • artificial neural network
  • bibliometric
  • offshore

ASJC Scopus subject areas

  • Computer Science (all)
  • Chemical Engineering (all)
  • Engineering (all)


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