Hybrid AI–FEA Framework for Seismic Assessment of Confined Masonry Walls Using Crack Image-Based Material Property Inference

Recent advances in computer vision and artificial intelligence have enabled new approaches for non-destructive post-earthquake assessment of masonry structures. This study proposes a hybrid AI–FEA framework that integrates a MobileNetV2 convolutional neural network for crack-image-based material property inference with nonlinear finite element analysis (FEA) of confined masonry walls. The model predicts key mechanical parameters, including elastic modulus, compressive and tensile strengths, and fracture energies, directly from crack morphology, and these parameters are subsequently used as input for DIANA FEA to simulate the wall’s seismic response. The framework is validated against reference experimental data, achieving a strong parametric correlation (R2 = 0.91) and accurately reproducing characteristic nonlinear behavior such as stiffness degradation, diagonal cracking, and post-peak softening in pushover analysis. Photographs from the Limatambo urban area in Lima, Peru, are included to illustrate typical damage patterns in a high-seismic-risk context, although the numerical model represents a standardized confined masonry wall typology rather than site-specific buildings. The proposed methodology offers a consistent, non-destructive, and efficient tool for seismic performance evaluation and supports the digital modernization of structural diagnostics in earthquake-prone regions.