{"ID":2899828,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.02005","arxiv_id":"2507.02005","title":"Discovery of Fatigue Strength Models via Feature Engineering and automated eXplainable Machine Learning applied to the welded Transverse Stiffener","abstract":"This research introduces a unified approach combining Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to predict fatigue strength in welded transverse stiffener details. It integrates expert-driven feature engineering with algorithmic feature creation to enhance accuracy and explainability. Based on the extensive fatigue test database regression models - gradient boosting, random forests, and neural networks - were trained using AutoML under three feature schemes: domain-informed, algorithmic, and combined. This allowed a systematic comparison of expert-based versus automated feature selection. Ensemble methods (e.g. CatBoost, LightGBM) delivered top performance. The domain-informed model $\\mathcal M_2$ achieved the best balance: test RMSE $\\approx$ 30.6 MPa and $R^2 \\approx 0.780% over the full $Δσ_{c,50\\%}$ range, and RMSE $\\approx$ 13.4 MPa and $R^2 \\approx 0.527% within the engineering-relevant 0 - 150 MPa domain. The denser-feature model ($\\mathcal M_3$) showed minor gains during training but poorer generalization, while the simpler base-feature model ($\\mathcal M_1$) performed comparably, confirming the robustness of minimalist designs. XAI methods (SHAP and feature importance) identified stress ratio $R$, stress range $Δσ_i$, yield strength $R_{eH}$, and post-weld treatment (TIG dressing vs. as-welded) as dominant predictors. Secondary geometric factors - plate width, throat thickness, stiffener height - also significantly affected fatigue life. This framework demonstrates that integrating AutoML with XAI yields accurate, interpretable, and robust fatigue strength models for welded steel structures. It bridges data-driven modeling with engineering validation, enabling AI-assisted design and assessment. Future work will explore probabilistic fatigue life modeling and integration into digital twin environments.","short_abstract":"This research introduces a unified approach combining Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to predict fatigue strength in welded transverse stiffener details. It integrates expert-driven feature engineering with algorithmic feature creation to enhance accuracy and explainab...","url_abs":"https://arxiv.org/abs/2507.02005","url_pdf":"https://arxiv.org/pdf/2507.02005v1","authors":"[\"Michael A. Kraus\",\"Helen Bartsch\"]","published":"2025-07-01T21:57:12Z","proceeding":"cs.CE","tasks":"[\"cs.CE\",\"cs.AI\"]","methods":"[]","has_code":false}