Probabilistic fatigue stiffness variation of angle-ply GFRP composites considering stress ratio effect

Fiber-reinforced polymer (FRP) composites are frequently used in bridges, wind power installations, and marine floating structures, and there is a growing interest in understanding their fatigue behavior. However, due to a considerable dispersion of the fatigue properties of FRP composites, reliable tools for probabilistic prediction of the fatigue stiffness are required. Thus, in this work, it is proposed a method that uses Bayesian inference and Markov Chain Monte Carlo (MCMC) simulations to estimate the degradation of the stiffness in FRP composites. This methodology is suggested not only to predict the fatigue residual stiffness, but also to consider the reliability of such predictions. Currently, the existing models for stiffness degradation in FRP composites need to fit the model parameters separately under different stress ratios. To address this, a correction term is proposed to describe the relationship between the stress level, stress ratio, fatigue cycles, and residual stiffness. The results achieved validate and prove the effectiveness of the suggested stiffness model, which incorporates four material parameters to calculate the residual fatigue stiffness of FRP composites.