Advances in machine learning for predicting fatigue behavior: from material properties to fatigue life and fatigue crack growth

Purpose The purpose of this review is to evaluate the transformative potential of machine learning (ML) in predicting fatigue behavior. The review seeks to highlight current progress, practical applications, and emerging challenges, offering a roadmap for leveraging ML to improve reliability, accuracy, and efficiency in fatigue-related engineering analyses. Design/methodology/approach This review systematically examines the application of machine learning (ML) techniques to fatigue behavior prediction, encompassing material properties, fatigue life, and crack growth. It employs a comprehensive survey of recent advancements, focusing on data preprocessing, regression methods, deep learning architectures, and hybrid approaches that integrate physics-based models with ML. Multiscale modeling and ensemble techniques are also analyzed for their potential to enhance prediction accuracy and reliability. Findings The review reveals that ML techniques significantly enhance fatigue behavior prediction by addressing complex, multiscale, and nonlinear characteristics of materials. Case studies demonstrate successful ML applications in aerospace and civil engineering, underscoring its practical value. However, challenges such as limited data quality, model interpretability, and computational scalability persist, necessitating further innovation and interdisciplinary collaboration to fully realize ML’s potential. Originality/value This review provides a comprehensive and up-to-date analysis of machine learning (ML) applications in fatigue behavior prediction, addressing material properties, fatigue life, and crack growth. It bridges the gap between traditional fatigue analysis methods and data-driven approaches, emphasizing the integration of physics-based insights with ML for enhanced accuracy and reliability.