Probabilistic fatigue models are required to account conveniently for the several sources of uncertainty arising in the prediction procedures, such as the scatter in material behavior. In this paper, a recently proposed stress-based probabilistic model is assessed using fatigue data available for the P355NL1 steel (a pressure vessel steel). The referred probabilistic model is a log-Gumbel regression model, able to predict the probabilistic Wöhler field (P–S–N field), taking into account the mean stress (or stress R-ratio) effects. The parameters of the probabilistic model are identified using stress-life data derived for the P355NL1 steel, from smooth specimens, for three distinct stress R-ratios, namely R = −1, R = −0.5, and R = 0. The model requires a minimum of two test series with distinct stress R-ratios. Since data from three test series is available, extrapolations are performed to test the adequacy of the model to make extrapolations for stress R-ratios other than those used in the model parameters assessment. Finally, the probabilistic model is used to model the fatigue behavior of a notched plate made of P355NL1 steel. In particular, the P–S–N field of the plate is modeled and compared with available experimental data. Cyclic elastoplastic analysis of the plate is performed since plasticity at the notch root is developed. The probabilistic model correlated appropriately the stress-life data available for the P355NL1 steel and was able to perform extrapolations for stress ratios other than those used in the model identification. The P–S–N field identified using data from smooth specimens led to consistent predictions of the P–S–N field for a notched plate, demonstrating the adequacy of the probabilistic model also to predict the probabilistic Wöhler field for notched components.

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