Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 2
Articles | Volume 11, issue 2
https://doi.org/10.5194/wes-11-443-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-443-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 2
Research article
 | 
10 Feb 2026
Research article |  | 10 Feb 2026

A machine-learning-based approach for better prediction of fatigue life of offshore wind turbine foundations using smaller data sizes

Ahmed Mujtaba, Wout Weijtjens, Negin Sadeghi, and Christof Devriendt

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Short summary
This study proposes a random-forest-based machine learning (ML) model for fatigue life prediction of offshore wind turbine monopile foundations compared to traditional approaches, using long-term strain data from turbines in the Belgian North Sea. This study shows that the ML model predicts the fatigue life of monopile foundations more reliably when only short-term measurements are available, whereas for longer monitoring periods of greater than 12 months, simpler binning methods perform equally well.
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