2025/07/01 by Kraus, Michael A., Bartsch, Helen
#Artificial Intelligence (cs.AI) #Computational Engineering #FOS: Computer and information sciences #Finance #and Science (cs.CE)
paper · doi:10.48550/arxiv.2507.02005
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 M2 achieved the best balance: test RMSE ≈ 30.6 MPa and R2 ≈ 0.780% over the full Δσc,50% range, and RMSE ≈ 13.4 MPa and R2 ≈ 0.527% within the engineering-relevant 0 - 150 MPa domain. The denser-feature model (\mathcal M3) showed minor gains during training but poorer generalization, while the simpler base-feature model (\mathcal M1) performed comparably, confirming the robustness of minimalist designs. XAI methods (SHAP and feature importance) identified stress ratio R, stress range Δσi, yield strength ReH, 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.