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AuthorNashed, Mohamad Shadi
AuthorRenno, Jamil
AuthorMohamed, M. Shadi
Available date2022-06-14T05:20:53Z
Publication Date2022-06
Publication NameFatigue and Fracture of Engineering Materials and Structures
Identifierhttp://dx.doi.org/10.1111/ffe.13759
CitationNashed, MS, Renno, J, Mohamed, MS. Modelling fatigue uncertainty by means of nonconstant variance neural networks. Fatigue Fract Eng Mater Struct. 2022; 1- 13. doi:10.1111/ffe.13759
ISSN8756-758X
URIhttp://hdl.handle.net/10576/32090
AbstractThe 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.
SponsorFinancial support for this research was graciously provided by Qatar National Research Fund (a member of Qatar Foundation) via the National Priorities Research Project under grant NPRP-11S-1220-170112. Open Access funding was graciously provided by the Qatar National Library.
Languageen
PublisherWiley
Relationhttp://hdl.handle.net/10576/29984
Subjectmachine learning (ML)
probabilistic neural networks (PNNs)
nonconstant variance
fatigue modelling
TitleModelling fatigue uncertainty by means of nonconstant variance neural networks
TypeArticle
Pagination1- 13
ESSN1460-2695
dc.accessType Open Access


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