Modelling Fatigue Uncertainty by Means of Nonconstant Variance Neural Networks

Mohamad Nashed, Jamil Renno, M. Shadi Mohamed

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
41 Downloads (Pure)


The modelling of fatigue using machine learning (ML) has been gaining traction in the engineering community. Among ML techniques, the use of probabilistic neural networks (PNNs) has recently emerged as a candidate for modelling fatigue applications. In this paper, we use PNNs with nonconstant variance to model fatigue. We present two case studies to demonstrate the developed approach. First, we model the fatigue life of cover-plated beams under constant amplitude loading, and then we model the relationship between random vibration velocity and equivalent stress in process pipework. The two case studies demonstrate that PNNs with nonconstant variance can model the distribution of the data while also considering the variability of both distribution parameters (mean and standard deviation). This shows the potential of PNNs with nonconstant variance in modelling fatigue applications. All the data and code used in this paper are openly available.
Original languageEnglish
Pages (from-to)2468-2480
Number of pages13
JournalFatigue and Fracture of Engineering Materials and Structures
Issue number9
Early online date12 Jun 2022
Publication statusPublished - Sept 2022


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