316L(N) Creep modeling with phenomenological approach and artificial intelligence based methods

Research output: Contribution to journalArticlepeer-review


  • Daniele Baraldi
  • Stefan Holmström
  • Karl-Fredrik Nilsson
  • Matthias Bruchhausen
  • Igor Simovovski
  • EC - European Commission
  • EC - JRC - European Commission - Joint Research Centre
  • JRC - Joint Research Centre

Documents & links



A model that describes creep behavior is essential in the design or life assessment of components and systems that operate at high temperatures. Using the RCC-MRx data and the LCSP (logistic creep strain prediction) model, processed design data were generated over the whole creep regime of 316L(N) steel—i.e., primary, secondary, and tertiary creep. The processed design data were used to develop three models with different approaches for the creep rate: a phenomenological approach; an artificial neural network; and an artificial intelligence method based on symbolic regression and genetic programming. It was shown that all three models are capable of describing the true creep rate as a function of true creep strain and true stress over a wide range of engineering stresses and temperatures without the need of additional micro-structural information. Furthermore, the results of finite element simulations reproduce the trends of experimental data from the literature Special Issue: https://www.mdpi.com/journal/metals/special_issues/creep_deformation_elevated_temperatures


Original languageEnglish
Pages (from-to)1-24
Number of pages24
Issue number5
Publication statusPublished - 24 Apr 2021


  • Creep model, 316L(N), LSCP model, Neural network, Machine learning, Phenomenological approach, Austenitic stainless steel

ID: 7126268