A dynamic criterion for failure probability prediction of GFRP laminates using Lamb wave velocity with improved accuracy and consistency

Abstract

This paper presents a dynamic criterion for improving the performance on fatigue life and failure probability prediction of GFRP laminates using Lamb wave velocity. A statistic damage model is built upon a Lamb wave velocity degradation model by introducing a normal distributed random variable to represent the uncertainties. The posterior distributions of the model parameters are obtained by Bayesian inference and Metropolis-Hastings algorithm with noninformative and informative prior distributions. Most importantly, a dynamic criterion is proposed to obtain a specimen-specific failure threshold value based on an assumption that the critical damage stage (CDS) of each specimen differs due to the randomicity of initial flaws. Experimental validation is conducted to compare the predicted fatigue life with both the dynamic criterion and traditional static criterion. The results show that applications of the dynamic criterion can improve the predicting accuracy and consistency, especially during early stages when available data is limited.