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المؤلفNashed, Mohamad Shadi
المؤلفMohamed, M Shadi
المؤلفShady, Omar Tawfik
المؤلفRenno, Jamil
تاريخ الإتاحة2024-06-02T06:20:08Z
تاريخ النشر2022
اسم المنشورFatigue and Fracture of Engineering Materials and Structures
المصدرScopus
المعرّفhttp://dx.doi.org/10.1111/ffe.13660
الرقم المعياري الدولي للكتاب8756758X
معرّف المصادر الموحدhttp://hdl.handle.net/10576/55704
الملخصMany 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 (https://github.com/MShadiNashed/probabilistic-machine-learning-for-fatigue-data).
راعي المشروعThis project was graciously sponsored by the Qatar National Research Fund (a member of Qatar Foundation) via the National Priorities Research Project under grant NPRP-11S-1220-170112.
اللغةen
الناشرJohn Wiley and Sons Inc
الموضوعartificial neural network (ANN)
failure probability
fatigue
fatigue life prediction
probabilistic method
vibration
العنوانUsing probabilistic neural networks for modeling metal fatigue and random vibration in process pipework
النوعArticle
الصفحات1227-1242
رقم العدد4
رقم المجلد45
dc.accessType Abstract Only


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