A CGAN Framework for Predicting Crack Patterns and Stress-Strain Behavior in Concrete Random Media

Random media like concrete and ceramics exhibit stochastic crack propagation due to their heterogeneous microstructures. This study establishes a Conditional Generative Adversarial Network (CGAN) combined with random field modeling for the efficient prediction of stochastic crack patterns and stress-strain responses. A total dataset of 500 samples, including crack propagation images and corresponding stress-strain curves, is generated via random Finite Element Method (FEM) simulations. This dataset is then partitioned into 400 training and 100 testing samples. The model demonstrates robust performance with Intersection over Union (IoU) scores of 0.8438 and 0.8155 on training and testing datasets, and R2 values of 0.9584 and 0.9462 in stress-strain curve predictions. By using these results, the CGAN is subsequently implemented as a surrogate model for large-scale Monte Carlo (MC) Simulations to capture the key statistical characteristics such as crack density and spatial distribution. Compared to conventional FEM-based methods, this approach reduces the computational cost to about 1/250 while maintaining high prediction accuracy. The methodology establishes a viable pathway for probabilistic fracture analysis in quasi-brittle materials, balancing computational efficiency with physical fidelity in capturing material stochasticity.