AI 中文总结
本研究针对震后损伤评估的可迁移性挑战，提出物理信息特征融合与结构元数据集成框架，经实验验证该框架可提升损伤分类性能，助力社区恢复评估。
AI 中文摘要
地震引发的结构损伤评估仍是基于人口的结构健康监测（PBSHM）的关键挑战，其中损伤表征必须能在异构建筑间泛化。现有基于振动的方法常依赖特定结构的损伤敏感特征（DSFs）和统一漂移阈值，限制了可迁移性。本研究提出一种用于震后损伤分类和韧性导向评估的物理信息特征融合与结构元数据集成框架。针对具有不同几何和材料属性的建筑群体，在多种地震场景下生成非线性模拟数据集。为获得一致标签，通过非线性推覆分析和基于承载力的阈值而非固定漂移极限定义损伤状态。利用稀疏的地面和屋顶加速度测量，在多机器学习分类器的分组跨结构验证下，将物理信息DSFs与Catch22和MiniRocket表征进行比较。结果表明，在稀疏传感条件下，物理信息DSFs优于通用时间序列表征；结构元数据通过为解释基于响应的DSFs提供上下文，进一步提升了鲁棒性；模态信息在模拟中增益有限，但在分布外的振动台验证中显著提升性能，有助于弥合模拟与实验的差异。
英文摘要
Earthquake-induced structural damage assessment remains a key challenge for population-based Structural Health Monitoring (PBSHM), where damage representations must generalize across heterogeneous buildings. Existing vibration-based methods often rely on structure-specific damage-sensitive features (DSFs) and uniform drift thresholds, limiting transferability. This study proposes a physics-informed feature fusion and structural metadata integration framework for post-earthquake damage classification and resilience-oriented assessment. A nonlinear simulation dataset is generated for building populations with varied geometrical and material properties under multiple earthquake scenarios. To obtain consistent labels, damage states are defined through nonlinear pushover analysis and capacity-based thresholds rather than fixed drift limits. Using sparse ground and roof acceleration measurements, physics-informed DSFs are compared with Catch22 and MiniRocket representations under group-wise cross-structure validation with multiple machine-learning classifiers. Results show that physics-informed DSFs outperform generic time-series representations under sparse sensing. Structural metadata further improves robustness by providing context for interpreting response-based DSFs. Modal information offers limited gains in simulations but substantially improves out-of-distribution shaking-table validation, helping bridge simulation-experiment discrepancies.
