Seismic assessment of unreinforced masonry façades from images using macroelement-based modeling.

Despite the variability of urban infrastructure, unreinforced masonry buildings remain globally prevalent. Constructed from brick, hollow concrete blocks, stone, or other masonry materials, these structures account for a significant proportion of fatalities during seismic events-particularly in regions with limited access to early warning systems. Due to the complex behavior of masonry, accurately assessing structural vulnerabilities is highly dependent on the chosen modeling strategy. Yet, scalable, cost-effective approaches based on simple RGB imagery can still offer valuable insights. In this context, building on a previously developed digitalization methodology, this study proposes an automated, image-based framework for the rapid, non-invasive seismic evaluation of façades, addressing important research gaps in disaster resilience. The framework links image data with structural simulation by extracting visual and geometric features and translating them into consistent macroelement models using computer vision techniques, enabling nonlinear analyses under in-plane cyclic loading. The adopted numerical strategy has been extensively validated in prior work, with predictions closely aligning with experimental results. While the outcomes are predictive rather than diagnostic, future integration with publicly accessible urban imagery may enable the development of real-time, cross-border seismic risk maps.