TY  - EJOU
AU  - Kawkabi, Khaja Wahaajuddin 
AU  - Nziko, Andre Steve  Talla 
AU  - Ngangura, Prince Manyanya 
AU  - Long, Xu 

TI  - Morphology-aware CNN with Statistical Descriptor Regression and Conditional Image Synthesis for Predicting Crack Patterns in Steel Fiber-Reinforced Concrete
T2  - Computers, Materials \& Continua

PY  - 
VL  - 
IS  - 
SN  - 1546-2226

AB  - Accurate characterization of crack evolution in steel fiber-reinforced concrete (SFRC) remains a fundamental challenge due to heterogeneous mesostructure and the highly nonlinear nature of fracture processes governed by fiber–matrix interactions. This paper proposes a morphology-aware two-stage framework that reformulates the ill-posed curve-to-image mapping into two well-posed steps. Stage 1 (StatNet) learns to regress an eleven-component vector of crack morphology descriptors directly from uniaxial stress-strain response and mix-design metadata using dedicated encoders. Stage 2 (PatternDecoder) synthesizes high-resolution crack-probability maps conditioned on predicted descriptors and a latent embedding of the stress-strain curve. The framework is trained and validated on 220 meso-scale Abaqus cohesive-zone model simulations with explicit fiber beam elements. The model achieves a global pooled coefficient of <i>R</i><sup>2</sup> = 0.940 across all morphology descriptors on the held-out validation folds, per-sample inference time of 14.57 ms. Comparisons against DirectCNN (direct convolutional neural network) and CGAN (conditional generative adversarial network) baselines confirm the superiority of the proposed approach in terms of structural fidelity, parameter efficiency, and computational cost. Residual diagnostics confirm homoscedasticity and near-normal errors, while gradient sensitivity analysis highlights physically consistent transitions at peak stress. The morphology-aware decomposition transforms an ill-posed mapping into an interpretable, computationally efficient surrogate model that bridges mesoscale fracture mechanisms with rapid crack-pattern assessment.
KW  - Steel fiber-reinforced concrete; crack pattern prediction; morphology-aware CNN; surrogate modeling; cohesive zone model

DO  - 10.32604/cmc.2026.086951