This study presents an artificial intelligence–assisted visual inspection procedure and a preliminary resilience assessment technique for the post-earthquake evaluation of masonry structures affected by major Italian earthquakes since 1980. A dataset of 250 images, collected during official surveys conducted by the Italian Civil Protection Department, was analyzed to automatically identify earthquake-induced damage patterns in spatial masonry components. The images, acquired both inside and outside damaged buildings and domed structures, were divided into training, validation, and test sets. The proposed methodology, although still at a preliminary stage due to the limited size of the dataset employed, aims to advance the use of artificial intelligence as a decision-support tool for enhancing structural resilience in post-earthquake scenarios, with particular attention to historic masonry constructions. After training, the AI model achieved a strong ability to correctly identify earthquake-induced damage patterns in masonry structures. The model was also deployed for inference on previously unseen test images, where the predicted bounding boxes qualitatively confirmed its effectiveness in detecting damage patterns. From a mechanical standpoint, the proposed approach supports the formulation of discrete no-tension models for masonry walls and domes affected by seismic events, based on the damage predictions provided by the AI-assisted detection procedure, which are subsequently translated into mechanical representations through engineering-driven post-processing operations. A recently developed strut-and-net approach is then employed to verify the existence of a network of compressed masonry struts capable of sustaining the vertical and horizontal loads acting on the examined structural systems.
Fraternali et al. (2026) studied this question.