Time-varying structural reliability assessment method: Application to fiber reinforced composites under repeated impact loading

Abstract

Reliability evaluations play a significant role in engineering applications to ensure the serviceability and safety of advanced structures such as those made of composites. Here, a dynamic reliability evaluation analysis based on the probability density evolution Method (PDEM) has been adapted to assess the reliability of composite structures under uncertainties within the material properties and the external loadings. A Back-Propagation Neural Network approach is employed to identify the system's nonlinear structural response, which is often the case under large deformations. To exemplify, a split Hopkinson pressure bar system was employed to mimic the mechanical behavior of a polypropylene/fiberglass woven composite plate structure under repeated high-strain rate impacts. Subsequently, the reliability prediction was performed offline via the system model and integration of uncertainties, as well as via an online SHM-based approach, and compared to full-scale (direct) experimental reliability values by repeating the impact tests on a population of samples. A material degradation factor has been introduced within the PDEM approach to account for surface damage induced during impacts. Results clearly showed the accuracy of the PDEM in predicting the remaining reliability of the composite after each impact. The method is generic and may be applied to other types of loadings and structures.