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• W4206915526 abstract "Abstract 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 ( https://github.com/MShadiNashed/probabilistic-machine-learning-for-fatigue-data )." @default.
• W4206915526 created "2022-01-26" @default.
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• W4206915526 date "2022-01-23" @default.
• W4206915526 modified "2023-09-27" @default.
• W4206915526 title "Using probabilistic neural networks for modeling metal fatigue and random vibration in process pipework" @default.
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• W4206915526 doi "https://doi.org/10.1111/ffe.13660" @default.
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