SIFBench: An Extensive Benchmark for Fatigue Analysis

2025-06-01

AI Summary

Fatigue-induced crack propagation is a primary cause of structural failure in safety-critical domains such as aerospace, where accurate prediction of the stress intensity factor (SIF) is essential for fatigue life assessment. Current machine learning (ML) applications in fracture mechanics are hindered by the absence of high-quality, standardized, large-scale, publicly available datasets. To address this, we introduce SIFBench—the first open-source benchmark database dedicated to SIF prediction—comprising over 5 million crack-structure geometric configurations and high-fidelity finite element simulation results across 37 configuration categories. We establish the first standardized, scalable, and fully public SIF prediction benchmark, accompanied by a unified Python interface and comprehensive evaluation templates. Leveraging SIFBench, we systematically evaluate multiple ML models—including random forests, support vector machines, feedforward neural networks, and Fourier neural operators—demonstrating substantial improvements in predictive accuracy and significantly lowering the barrier to deploying ML in damage-tolerant design and predictive maintenance.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: Safety and Robustness
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Lack of rich datasets for ML-based fatigue crack prediction
• Need for accurate stress intensity factor (SIF) prediction
• Absence of standardized benchmark for damage tolerance design

Innovation

• Open-source large-scale benchmark database
• High-fidelity finite element simulations
• Unified Python interface for data