用AI自动建模焊接加劲肋疲劳强度，又准又可解释。
Discovery of Fatigue Strength Models via Feature Engineering and automated eXplainable Machine Learning applied to the welded Transverse Stiffener
- 结合专家特征与算法生成特征，用AutoML训练预测模型。
- 最佳模型在150MPa内误差仅13.4MPa，R²达0.527。
- 揭示应力比、焊后处理等关键影响因素，适合工程设计参考。
本研究提出一种融合自动化机器学习（AutoML）与可解释人工智能（XAI）的统一方法，用于预测焊接横向加劲肋的疲劳强度。通过专家驱动特征工程与算法生成特征相结合，基于大规模疲劳试验数据库，训练了梯度提升、随机森林和神经网络等回归模型，采用三种特征方案：领域知识型、算法生成型和混合型，系统比较了专家与自动特征选择的效果。集成方法（如CatBoost、LightGBM）表现最优。领域知识模型$/mathcal M_2$在全Δσ_{c,50%}范围内实现测试均方根误差≈30.6 MPa、R²≈0.780%，在工程关注的0–150 MPa区间内误差≈13.4 MPa、R²≈0.527%。更密集特征模型（$/mathcal M_3$）虽训练性能略优但泛化能力差，基础特征模型（$/mathcal M_1$）表现相当，验证了简约设计的鲁棒性。SHAP与特征重要性分析表明，应力比R、应力幅Δσ_i、屈服强度R_{eH}及焊后处理（TIG修磨对比原焊态）是主导因子，板宽、喉厚、加劲肋高度等几何因素亦显著影响疲劳寿命。该框架证明，结合AutoML与XAI可构建准确、可解释且稳健的焊接钢结构疲劳强度模型，推动数据驱动建模与工程验证融合，支持智能设计与评估。未来将探索概率疲劳寿命建模及数字孪生集成。
原文摘要 · Abstract (English)
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.
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