Using probabilistic neural networks for modeling metal fatigue and random vibration in process pipework

Mohamad Nashed, M. Shadi Mohamed, Omar Tawfik Shady, Jamil Renno

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
26 Downloads (Pure)


Many experiments are usually needed to quantify probabilistic fatigue behavior in metals. Previous attempts used separate artificial neural network (ANN) to calculate different probabilistic ranges which can be computationally demanding for building probabilistic fatigue constant life diagram (CLD). Alternatively, we propose using probabilistic neural network (PNNs) which can capture data distribution parameters. The resulted model is generative and can quantify aleatoric uncertainty using a single network. Two tests are presented. The first captures the fatigue life aleatoric uncertainty for P355NL1 steel and successfully builds a probabilistic fatigue CLD. The resulted network is not only more efficient but also provides higher accuracy compared with ANN. To assess fatigue, the second test examines vibrations of a pipework assembly. The proposed methodology quantifies the nonlinear relation between the vibration velocity and the equivalent stress and successfully reflects measurements uncertainties in fatigue assessment. The proposed methodology is published in opensource format.
Original languageEnglish
Pages (from-to)1227-1242
Number of pages16
JournalFatigue and Fracture of Engineering Materials and Structures
Issue number4
Early online date23 Jan 2022
Publication statusPublished - Apr 2022


  • artificial neural network (ANN)
  • failure probability
  • fatigue
  • fatigue life prediction
  • probabilistic method
  • vibration

ASJC Scopus subject areas

  • Materials Science(all)
  • Mechanics of Materials
  • Mechanical Engineering


Dive into the research topics of 'Using probabilistic neural networks for modeling metal fatigue and random vibration in process pipework'. Together they form a unique fingerprint.

Cite this