A Neural Network Framework for Predicting Durability and Damage Tolerance of Polymer Composites under Combined Hygrothermal-mechanical Loading

Abstract

Fiber-reinforced polymer (FRP) composites are used in crucial structures which are susceptible to a combination of mechanical (static/dynamic) and hygrothermal (moisture absorption and temperature) loads. This research presents a novel artificial neural network (ANN) framework that employs the dielectric permittivity response of FRP composites under combined mechanical-hygrothermal loading to predict the extent of moisture absorption, fatigue life, and remaining useful life. The proposed framework is based on the phenomenological and data-driven study of the effects of static and dynamic mechanical loads along with moisture absorption in the dielectric characteristics of these composites.