Structural health monitoring (SHM) is essential for managing infrastructure by continuously monitoring performance. To address the complexity of SHM for large systems, this study introduces a dynamic graph neural network (DynGNN) approach for near-real-time damage identification. The approach represents the infrastructure as a graph and uses a dynamic adjacency matrix based on proper orthogonal decomposition (POD) to capture the dynamic characteristics and spatial correlations of responses. The DynGNN model has an autoencoder architecture with one encoder for latent feature extraction and two decoders for capturing spatiotemporal changes and is trained on intact structural responses. The proposed approach employs a damage index based on reconstruction errors for damage identification. In applications to a steel truss bridge under vehicle loads, the proposed approach is tested by real-time simulation using the field test data obtained from the damaged bridge. By incorporating a dynamic adjacency matrix, the results successfully demonstrate that the proposed approach effectively adapts to the time-varying nature of structural responses and can accurately identify damage in near-real time. The novel integration of a dynamic graph structure with POD allows the proposed method to capture both spatial and temporal variations, providing a significant improvement over traditional static methods.

中文翻译：

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基于适当正交分解的动态图神经网络在车辆载荷下的近实时损伤识别


结构健康监控 （SHM） 对于通过持续监控性能来管理基础设施至关重要。为了解决大型系统 SHM 的复杂性，本研究引入了一种动态图神经网络 （DynGNN） 方法，用于近乎实时的损伤识别。该方法将基础设施表示为图形，并使用基于适当正交分解 （POD） 的动态邻接矩阵来捕获响应的动态特征和空间相关性。DynGNN 模型具有自动编码器架构，其中一个编码器用于潜在特征提取，两个解码器用于捕获时空变化，并针对完整的结构响应进行训练。所提出的方法采用基于重建误差的损伤指数进行损伤识别。在车辆载荷下的钢桁架桥应用中，使用从受损桥梁获得的现场测试数据通过实时仿真对所提出的方法进行了测试。通过结合动态邻接矩阵，结果成功地证明了所提出的方法有效地适应了结构响应的时变性质，并且可以近乎实时地准确识别损伤。动态图结构与 POD 的新颖集成使所提出的方法能够捕获空间和时间变化，与传统的静态方法相比，提供了显着的改进。