An efficient micromechanical framework for elastic and viscoelastic properties prediction of unidirectional continuous fiber-reinforced polymers with microvoids

• A fully analytical K-BR framework is proposed to predict the elastic and viscoelastic properties of UDFRPs with microvoids. • The model quantifies the effect of microvoid spatial distribution (distinguishing inter-matrix vs. fiber-adjacent) via novel ’volume weakening factors’. • K-BR model predictions show excellent agreement with three-phase ’random reduction method’ FEM benchmark data. • The model significantly outperforms existing micromechanical models, especially for matrix-dominated transverse properties. • The K-BR framework provides a high-fidelity, high-efficiency, and reliable alternative to computationally intensive FEM simulations. Microvoids in unidirectional continuous fiber-reinforced polymers are inevitable defects. While precise RVE-based numerical simulations are computationally prohibitive, existing efficient micromechanical methods lack accuracy in accounting for complex void weakening effects. To bridge this gap, the present study proposes an efficient micromechanical framework (K-BR) based on a two-step homogenization strategy. First, a Kerner-enhanced generalized Maxwell model homogenizes the porous matrix into an ’equivalent matrix’. Subsequently, an extended bridging model, modified by innovative ’volume weakening factors’, combines this ’equivalent matrix’ with fibers, simultaneously considering microvoid content and spatial distribution. The model comprehensively considers temperature, time-dependence, and microvoid characteristics (content and spatial distribution) to predict equivalent elastic and viscoelastic properties. Validation against existing models and finite element simulations confirms the framework’s superior accuracy: for fiber volume fractions of 20–60% and porosity below 10%, its average absolute relative error is approximately 3.9%. These results establish the K-BR framework as a high-fidelity and efficient alternative to computationally intensive numerical simulations. Moreover, this contribution deepens the physical understanding of microstructural interactions and has the potential to provide guidance for future model extensions and the optimal design of composite materials.