Integrated component and damage recognition for post-earthquake reinforced concrete structures via image segmentation

Accurate and efficient damage recognition of reinforced concrete structures is essential for structural health monitoring and recovery planning. However, most existing deep learning-based approaches treat damage recognition as an isolated visual task, without explicitly considering component-level context or the engineering relevance of different failure modes. This study proposes a multi-task framework, guided by the geometric and engineering characteristics of different damage types, that integrates component recognition with damage recognition to achieve fine-grained post-earthquake component assessment. Component localization is achieved through instance segmentation, region-level damage is obtained via semantic segmentation, and cracks are identified via orientation-aware bounding box detection. To address data scarcity, a data augmentation strategy termed foreground-background regrouping is introduced, which leverages structural domain knowledge to generate physically realistic damage samples. Experimental results demonstrate the effectiveness of the framework, yielding accurate component localization and damage segmentation. The framework shows strong potential for automated, rapid post-earthquake component assessment and decision-making support.