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All issues Volume 729 (2026) E3S Web Conf., 729 (2026) 07004 References
Open Access
Issue
E3S Web Conf.
Volume 729, 2026
1st Sustainable Power, Energy, Transportation, and Materials Conference (SPETM 2026)
Article Number 07004
Number of page(s) 8
Section 4IR Technologies in Smart Grid, Energy Systems, and Smart City Technologies
DOI https://doi.org/10.1051/e3sconf/202672907004
Published online 31 July 2026
  1. S. G. Roh and H. R. Choi. Differential-drive in pipe robot for moving inside urban gas pipelines, IEEE Trans on Robs, vol. 21, no. 1, pp.1–17, (2005), doi:10.1109/TRO.2004.838000 [Google Scholar]
  2. Y. Liu, W. Zhang, and L. Lin Ge. Pipe inspection robots: A review, IOP Conf. Ser. Mater. Sci. Eng., vol. 12, pp. 1–12, (2022), doi: 10.1088/1757-899X/1272/1/012016. [Google Scholar]
  3. H. A. Kishawy and H. A. Gabbar. Review of pipeline integrity management practices, Inti Jou of Pres Ves and Piping, vol. 87, no. 7, pp. 373–380, (2010), doi: 10.1016/J.IJPVP.2010.04.003. [Google Scholar]
  4. Y. Bai and Q. Bai. Subsea Pipeline Integrity and Risk Management, Gulf Prof. Pub, Oxford., 2014, Accessed: Jun. 06, (2026). [Online]. Available: https://doi.org/10.1115/L2822013 [Google Scholar]
  5. T. Hu and J. Guo. Development and application of new technologies and equipment for in-line pipeline inspection, Natl Gas Ind B, vol. 6, no. 4, pp. 404–411, (2019), doi: 10.1016/J.NGIB.2019.01.017. [Google Scholar]
  6. D. Chukwuemeka Wegner, A. K. Nicholas, O. Odoh, and K. Ayansiji. A Machine Learning-Enhanced Model for Predicting Pipeline Integrity in Offshore Oil and Gas Fields, Jour of Fron in Multi Res, vol. 2, no. 2, pp. 331–342, (2021), doi: 10.54660/.IJFMR.2021.2.2.331-342. [Google Scholar]
  7. J. Liu, Y. Tong, and J. Liu. Review of snake robots in constrained environments, Rob. Auton. Syst., vol. 141, p. 103785, (2021), doi: 10.1016/J.ROBOT.2021.103785. [Google Scholar]
  8. S. Kazeminasab, N. Sadeghi, V. Janfaza, M. Razavi, S. Ziyadidegan, and M. K. Banks. Localization, Mapping, Navigation, and Inspection Methods in In-Pipe Robots: A Review, IEEE Access, vol. 9, pp. 162035–162058, (2021), doi:10.1109/ACCESS.2021.3130233. [Google Scholar]
  9. M. Tavakoli, L. Marques, and A. T. D. Almeida. Development of an industrial pipeline inspection robot, Indus Robot, vol. 37, no. 3, pp. 309–322, (2010), doi: 10.1108/01439911011037721. [Google Scholar]
  10. D. C. Wegner, D. Oderinde, and V. Omine. Sustainability and Low-Carbon Transitions in Offshore Energy Systems: A Review of Inspection and Monitoring Challenges, Jou of Fron in Multi Res, vol. 4, no. 2, pp. 273–285, (2023), doi: 10.54660/.JFMR.2023.4.2.273-285. [Google Scholar]
  11. E. O. Nwulu, T. Y. Elete, O. V. Erhueh, O. A. Akano, and K. O. Omomo. Machine learning applications in predictive maintenance : Enhancing efficiency across the oil and gas industry, Inter Jour of Eng Res Upd, vol. 5, no. 1, pp. 013–027, (2023), doi: 10.53430/IJERU.2023.5.1.0017. [Google Scholar]
  12. V. N. Onyechi. Pipeline integrity and risk prevention: Real-time monitoring, structural health analytics, and failure mitigation in harsh operating environments, Magna Scientia Adv Res and Rev, vol. 3, no. 2, pp. 139–151, (2021), doi: 10.30574/msarr.2021.3.2.0093. [Google Scholar]
  13. M. Grieves and J. Vickers. Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems, Tran Pers on Comp Sys: New Findings and Approaches, pp. 85–113, (2016), doi: 10.1007/978-3-319-38756-7_4. [Google Scholar]
  14. M. Layouni, M. S. Hamdi, and S. Tahar. Detection and sizing of metal-loss defects in oil and gas pipelines using pattern-adapted wavelets and machine learning, Applied Soft Comp Journal, vol. 52, pp. 247–261, (2017), doi: 10.1016/J.ASOC.2016.10.040. [Google Scholar]
  15. E. B. Priyanka, S. Thangavel, X. Z. Gao, and N. S. Sivakumar. Digital twin for oil pipeline risk estimation using prognostic and machine learning techniques, J. Ind. Inf. Integr., vol. 26, p. 100272, (2022), doi:10.1016/J.JII.2021.100272. [Google Scholar]

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