Engineering structural damage often exhibits diverse and complex features across multiple scales within small-scale regions of interest (ROI), complicating post-earthquake assessments. This paper proposes an interpretable deep learning (DL) framework for semantic segmentation of multicategory damage. Energy-derivative attention modules are integrated into convolutional neural networks (CNNs) to enhance feature extraction of small-scale ROI. Geometrically consistent and focal-informed (GCF) loss function emphasizes the regions and boundaries of small-scale ROI, incorporating geometrical constraints of split line length, curvature, and area. Mosaic data augmentation method further mitigates feature imbalance. The proposed method outperforms the baseline with an mIoU increase from 80.67 % to 88.88 %. IoU for concrete spalling reaches 89.16 %, and for bar buckling improves to 82.96 %. The synergy of geometrical consistency, energy-derivative attention, and mosaic augmentation method significantly enhances CNN performance for multicategory damage. Finally, the framework is deployed in graphical user interface (GUI) software, enabling structural assessment of post-earthquake buildings.

中文翻译：

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几何一致的能量导数注意力 CNN 用于多类别结构损伤的语义分割

工程结构损伤通常在小尺度感兴趣区域 （ROI） 内的多个尺度上表现出多样化和复杂的特征，使震后评估复杂化。本文提出了一种可解释的深度学习 （DL） 框架，用于多类别损伤的语义分割。能量导数注意力模块被集成到卷积神经网络 （CNN） 中，以增强小规模 ROI 的特征提取。几何一致性和焦点知情 （GCF） 损失函数强调小规模 ROI 的区域和边界，包括分割线长度、曲率和面积的几何约束。Mosaic 数据增强方法进一步缓解了特征不平衡。所提出的方法优于基线，mIoU 从 80.67 % 增加到 88.88 %。混凝土剥落的 IoU 达到 89.16 %，钢筋屈曲的 IoU 提高到 82.96 %。几何一致性、能量导数注意力和马赛克增强方法的协同作用显着增强了 CNN 对多类别损伤的性能。最后，该框架部署在图形用户界面 （GUI） 软件中，支持对地震后建筑物进行结构评估。