Beiler, MartenMartenBeilerBauer, Niklas MichaelNiklas MichaelBauerBaumgartner, JörgJörgBaumgartnerBraun, MoritzMoritzBraun2026-01-082026-01-082025-12-24International Journal of Fatigue 206: 109459 (2026)https://hdl.handle.net/11420/60602Evaluating the fatigue life of welded joints under multiaxial loading is a key challenge in structural engineering. This study explores machine learning (ML) methods for predicting fatigue life and compares their performance against the novel super ellipse criterion, which is an analytical approach that aims to improve current design standard methods (e.g., Eurocode 3, IIW). Using a dataset of uniaxial and multiaxial fatigue tests with varying phase angles, ML models—including artificial neural networks and extreme gradient boosting (XGBoost)—are trained on features like stress amplitudes, phase differences, and material properties. Artificial neural networks provide high accuracy, while tree-based models like XGBoost offer better interpretability via model agnostic interpretation using Explainable Artificial Intelligence. Results show ML models can outperform traditional criteria, especially under non-proportional loading, but face limitations near the edges of the training data. This work highlights the potential and challenges of ML in fatigue prediction and highlights their value for enhancing the safety and reliability of welded structures.en0142-11232025Elsevierhttps://creativecommons.org/licenses/by/4.0/Artificial neural networkExplainable AIExtreme gradient boostingFatigue strength assessmentMultiaxial fatigueSHAP analysisTechnology::620: Engineering::620.1: Engineering Mechanics and Materials Science::620.11: Engineering MaterialsComputer Science, Information and General Works::006: Special computer methods::006.3: Artificial Intelligence::006.32: Neural NetworksJournal Articlehttps://doi.org/10.15480/882.1639410.1016/j.ijfatigue.2025.10945910.15480/882.16394Journal Article