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Fatigue analysis holds profound significance in the design and maintenance of offshore wind energy systems, especially within the framework for transitioning from oil and gas to renewable energies. Addressing the impact of fatigue life variability is essential when generating reliable S-N curves and establishing safe operational domains. In contrast to commonly applied global S-N approaches presented in standards, local approaches provide a more comprehensive understanding of the material's fatigue strength. This study implements various probabilistic methods for generating fatigue strength curves, including the guidelines recommended by ISO 12107, a two-parameter Weibull distribution, the Castillo & Fernández-Canteli (CFC) model, and a Bayesian method that incorporates a Markov Chain Monte Carlo algorithm. Using experimental data from literature for S355 (base) and S690QL (base and welded) steels, two distinct model fitting approaches – classical linear regression (CLR) and orthogonal linear regression (OLR) – were applied. Also, this study explores how corrosion affects fatigue strength by deriving fatigue strength curves that consider this influence. In practical scenarios, CLR is recommended for the design of new projects, whereas OLR is recommended for retrofitting purposes in order to leverage the structural capacity and fatigue resistance of materials and structures that have been in long-term operation. Based on this comparative analysis, the most conservative model for CLR is the two-parameter Weibull distribution, whereas the most conservative model for OLR is the Bayesian approach incorporating the Markov Chain Monte Carlo algorithm. These models are identified as particularly well-suited for high-cycle fatigue, predicting shorter fatigue lives and indicating a higher potential for fatigue damage, thereby enhancing fatigue strength modelling for current offshore materials.
Mendes et al. (Mon,) studied this question.