A Surrogate‐Assisted Multi‐Scale Framework for Characterizing the Stochastic Flexural Strength of Plain Woven Composites

ABSTRACT Plain woven composites in load‐bearing structures often exhibit scatter in flexural strength, due to multi‐scale variability in fiber distribution, yarn architecture, and the resulting spatial heterogeneity of material moduli and strength parameters. This study develops a surrogate‐assisted framework to characterize the propagation of micro‐/meso‐scale structural variability to macro‐scale stochastic flexural strength. Micro‐scale RVEs with randomly distributed fibers and meso‐scale woven units with varying yarn geometry are constructed to quantify their influence on effective moduli and strength parameters. An MLP‐based surrogate framework with output‐specific regression models is established to map stochastic geometric inputs to effective material properties, in which correlation‐constrained training is further introduced to improve fitting performance while retaining the inter‐property correlation trends observed in the multi‐scale simulations. The predicted stochastic material property field is then transferred to the structural finite element model through interpolation and shape function for flexural strength analysis. The proposed framework is assessed by three‐point bending tests for cross‐ply, quasi‐isotropic, and helicoidal laminates. Comparison between the MLP models with and without correlation constraint shows that the constraint mainly improves the fitting accuracy of several output parameters, with the largest relative improvement about 12% for representative outputs. The predicted mean flexural strengths show relative errors of 11.6%, 1.9%, and 5.7% for the three layups, respectively. The predicted coefficients of variation are consistently higher than the experimental values, especially for cross‐ply laminates. This indicates that the framework provides a preliminary estimation of the layup‐dependent trend of strength scatter, while giving conservative estimates of dispersion.