ArticleSensors (Basel, Switzerland)2023
Sensor Data Reconstruction for Dynamic Responses of Structures Using External Feedback of Recurrent Neural Network.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Structural monitoring data repair based on a long short-term memory neural network.Scientific reports · 2024Article
- DEMA: A Deep Learning-Enabled Model for Non-Invasive Human Vital Signs Monitoring Based on Optical Fiber Sensing.Sensors (Basel, Switzerland) · 2024Article
- Multi-Damage Detection in Composite Space Structures via Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- A Data-Driven Based Response Reconstruction Method of Plate Structure with Conditional Generative Adversarial Network.Sensors (Basel, Switzerland) · 2023Article
- Design and Verification of Deep Submergence Rescue Vehicle Motion Control System.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
Funding
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Abstract
An event of sensor faults in sensor networks deployed in structures might result in the degradation of the structural health monitoring system and lead to difficulties in structural condition assessment. Reconstruction techniques of the data for missing sensor channels were widely adopted to restore a dataset from all sensor channels. In this study, a recurrent neural network (RNN) model combined with external feedback is proposed to enhance the accuracy and effectiveness of sensor data reconstruction for measuring the dynamic responses of structures. The model utilizes spatial correlation rather than spatiotemporal correlation by explicitly feeding the previously reconstructed time series of defective sensor channels back to the input dataset. Because of the nature of spatial correlation, the proposed method generates robust and precise results regardless of the hyperparameters set in the RNN model. To verify the performance of the proposed method, simple RNN, long short-term memory, and gated recurrent unit models were trained using the acceleration datasets obtained from laboratory-scaled three- and six-story shear building frames.
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Registered trials
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