Prediction of Rail Rolling Contact Fatigue Crack Growth Based on Physical Model-data Driven Hybrid

Abstract:

Under repeated wheel-rail loading， rolling contact fatigue cracks in rails propagate along the rail surface， exhibiting a distributed evolution process involving physical initiation， stochastic diffusion， and subsequent growth. To predict the stochastic process of crack evolution， a hybrid crack development prediction method integrating physics-based modelling and data-driven approaches is proposed in this paper. Random parameters associated with crack initiation and diffusion are incorporated into the physical model of crack evolution. These parameters are estimated using an approximate Bayesian inference algorithm， and neural networks are further employed for data training. On this basis， a relationship between crack diffusion and track operational conditions is established by integrating physical mechanisms with stochastic data-driven modelling. The results show that， for a given curve radius R， the crack diffusion rate λ is positively correlated with the daily gross tonnage Gd， and the effect of Gd on λ becomes significant when R<650 m. In contrast， Gd has only a weak effect on the initial crack inclination angle θ， whereas R is negatively correlated with θ. Moreover， a decrease in R or an increase in Gd significantly advances initial crack diffuse time T0.