Prediction of residual stresses in girth welded pipes using an artificial neural network approach

Jino Mathew, R.J. Moat, S. Paddea, Michael E. Fitzpatrick, P.J. Bouchard

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)
68 Downloads (Pure)


Management of operating nuclear power plants greatly relies on structural integrity assessments for safety critical pressure vessels and piping components. In the present work, residual stress profiles of girth welded austenitic stainless steel pipes are characterised using an artificial neural network approach. The network has been trained using residual stress data acquired from experimental measurements found in literature. The neural network predictions are validated using experimental measurements undertaken using neutron diffraction and the contour method. The approach can be used to predict through-wall distribution of residual stresses over a wide range of pipe geometries and welding parameters thereby finding potential applications in structural integrity assessment of austenitic stainless steel girth welds.
Original languageEnglish
Pages (from-to)89-95
Number of pages6
JournalInternational Journal of Pressure Vessels and Piping
Publication statusPublished - 27 Jan 2017

Bibliographical note

Due to publisher policy, the full text is not available on the repository until the 27 January 2018.


  • Residual stress profile
  • Girth welds
  • Stainless steel
  • Neural network
  • Neutron diffraction
  • Contour method


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