Establishing a unified fatigue life prediction model and quantifying the uncertainty in the mechanical behavior of materials are critical to ensure the structural integrity and equipment performance. For the commonly-used strain-based fatigue methods, existing estimation methods exhibit inevitable deviations, while data-driven methods have shown poor extrapolation ability and interpretability. Therefore, this paper aims to develop a probabilistic framework for strain-based fatigue life prediction and uncertainty quantification (UQ) to provide an indication for fatigue design/assessment using interpretable machine learning (ML) techniques. Based on Shapley additive explanations (SHAP) and symbolic regression (SR), interpretable prediction models with concise expressions and outstanding prediction performance are established and optimized according to the priori physical knowledge. Moreover, accounting for the material variability, the probabilistic assessment with UQ excellently validates the prediction model, and quantifies the variability of ε-N curves. The proposed framework provides a valuable reference and shows promising prospects in fatigue design for engineering components.

中文翻译：

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使用可解释机器学习进行基于应变的疲劳寿命预测和不确定性量化的概率框架


建立统一的疲劳寿命预测模型并量化材料机械行为的不确定性对于确保结构完整性和设备性能至关重要。对于常用的基于应变的疲劳方法，现有的估计方法表现出不可避免的偏差，而数据驱动的方法表现出较差的外推能力和可解释性。因此，本文旨在为基于应变的疲劳寿命预测和不确定性量化 （UQ） 开发一个概率框架，以使用可解释机器学习 （ML） 技术为疲劳设计/评估提供指示。基于Shapley加法解释（SHAP）和符号回归（SR），根据先验物理知识建立和优化表达式简洁、预测性能突出的可解释预测模型。此外，考虑到材料可变性，UQ 的概率评估很好地验证了预测模型，并量化了 ε-N 曲线的可变性。所提出的框架为工程构件的疲劳设计提供了有价值的参考，并显示出广阔的前景。