期刊
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
卷 20, 期 4, 页码 2216-2228出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/1475921720988666
关键词
Structural health monitoring; novelty detection; cable force; spatiotemporal graph convolutional networks; learnable adjacency matrices
资金
- National Key R&D Program of China [2018YFB1600200]
- NSFC [51922034, 51678204, 51638007]
- Heilongjiang Natural Science Foundation for Excellent Young Scholars [YQ2019E025]
- Guangxi Science Base and Talent Program [710281886032]
A novel detection method using spatiotemporal graph convolutional networks was developed in this study to analyze cable forces from different cable dynamometers, providing an effective way to diagnose structural changes and damages in bridges.
The novelty detection of bridges using monitoring data is an effective technique for diagnosing structural changes and possible damages, providing a critical basis for assessing the structural states of bridges. As cable forces describe the state of cable-stayed bridges, a novelty detection method was developed in this study using spatiotemporal graph convolutional networks by analysing spatiotemporal correlations among cable forces determined from different cable dynamometers. The spatial dependency of the sensor network was represented as a directed graph with cable dynamometers as vertices, and a graph convolutional network with learnable adjacency matrices was used to capture the spatial dependency of the locally connected vertices. A one-dimensional convolutional neural network was operated along the time axis to capture the temporal dependency. Sensor faults and structural variations could be distinguished based on the local or global anomalies of the spatiotemporal model parameters. Faulty sensors were detected and isolated using weighted adjacency matrices along with diagnostic indicators of the model residuals. After eliminating the effect of the sensor fault, the underlying variations in the state of the cable-stayed bridge could be determined based on the changing data patterns of the spatiotemporal model. The application of the proposed method to a long-span cable-stayed bridge demonstrates its effectiveness in sensor fault localization and structural variation detection.
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