This invention discloses a rapid prediction method for rail
rolling contact fatigue damage in the field of rail damage prediction technology. The method includes establishing a three-dimensional elastoplastic wheel-rail cyclic rolling contact finite
element model, improving the cyclic plastic constitutive model, and simulating the stress-strain evolution of the rail. A rail stress-strain sample
library is generated through parametric sampling and batch calculation. A Physically Constrained
Generative Adversarial Network (PC-GAN) is constructed, embedding physical constraints such as
contact force conservation and energy consistency into the
loss function to achieve rapid and high-precision mapping from operating parameters to the stress-strain field. Based on the PC-AN prediction results, cumulative damage and fatigue life are calculated using an incremental multiaxial
fatigue damage model, and a damage distribution map of the entire
rail line is drawn, identifying high-risk sections. This invention deeply integrates physical modeling with
intelligent algorithms, achieving an order-of-magnitude improvement in rail
fatigue damage assessment efficiency while ensuring prediction accuracy, providing efficient and reliable
technical support for rail
life assessment and maintenance decisions across the entire
rail line.