Title: SIFBench: An Extensive Benchmark for Fatigue Analysis Show Chinese title

Title: SIFBench：疲劳分析的广泛基准

Abstract: Fatigue-induced crack growth is a leading cause of structural failure across critical industries such as aerospace, civil engineering, automotive, and energy. Accurate prediction of stress intensity factors (SIFs) -- the key parameters governing crack propagation in linear elastic fracture mechanics -- is essential for assessing fatigue life and ensuring structural integrity. While machine learning (ML) has shown great promise in SIF prediction, its advancement has been severely limited by the lack of rich, transparent, well-organized, and high-quality datasets. To address this gap, we introduce SIFBench, an open-source, large-scale benchmark database designed to support ML-based SIF prediction. SIFBench contains over 5 million different crack and component geometries derived from high-fidelity finite element simulations across 37 distinct scenarios, and provides a unified Python interface for seamless data access and customization. We report baseline results using a range of popular ML models -- including random forests, support vector machines, feedforward neural networks, and Fourier neural operators -- alongside comprehensive evaluation metrics and template code for model training, validation, and assessment. By offering a standardized and scalable resource, SIFBench substantially lowers the entry barrier and fosters the development and application of ML methods in damage tolerance design and predictive maintenance. Show Chinese abstract

Abstract: 疲劳诱导的裂纹扩展是航空航天、土木工程、汽车和能源等关键行业中结构失效的主要原因。 准确预测应力强度因子（SIFs）——即控制线弹性断裂力学中裂纹扩展的关键参数——对于评估疲劳寿命和确保结构完整性至关重要。 尽管机器学习（ML）在SIF预测方面显示出巨大潜力，但其发展严重受限于缺乏丰富、透明、组织良好且高质量的数据集。 为解决这一问题，我们推出了SIFBench，这是一个开源的大规模基准数据库，旨在支持基于ML的SIF预测。 SIFBench包含超过5百万种不同的裂纹和组件几何形状，这些来源于37个不同场景的高保真有限元模拟，并提供了一个统一的Python接口，以便于无缝访问和自定义数据。 我们报告了使用多种流行的ML模型（包括随机森林、支持向量机、前馈神经网络和傅里叶神经算子）的基线结果，以及全面的评估指标和用于模型训练、验证和评估的模板代码。 通过提供标准化和可扩展的资源，SIFBench大大降低了进入门槛，并促进了机器学习方法在损伤容限设计和预测性维护中的开发与应用。