Fatigue Life Prediction

Fatigue life prediction aims to accurately estimate the lifespan of materials or structures under cyclic loading, crucial for ensuring safety and optimizing maintenance schedules. Current research heavily utilizes machine learning, employing diverse architectures like artificial neural networks, support vector machines, and Gaussian process regression, often enhanced by techniques such as self-supervised learning to address data scarcity. These advancements improve prediction accuracy and efficiency compared to traditional methods, impacting diverse fields from aerospace engineering and civil infrastructure to sports science and medical diagnostics through improved design, maintenance strategies, and risk assessment.