Structural Damage Detection Based on the Correlation of Variational Autoencoder Neural Networks Using Limited Sensors

Identifying the structural state without baseline data is an important engineering problem in the field of structural health monitoring, which is crucial for assessing the safety condition of structures. In the context of limited accelerometers available, this paper proposes a correlation-based dama...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 24; H. 8; S. 2616
Hauptverfasser: Lin, Jun, Ma, Hongwei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 19.04.2024
MDPI
Schlagworte:
Algorithms
Artificial intelligence
Bridges
correlation
Deep learning
Efficiency
Identification
limited sensors
Neural networks
Numerical analysis
Reinforced concrete
Research methodology
Sensors
Simulation methods
structural damage detection
structural health monitoring
variational autoencoder neural networks
structural damage detection
structural health monitoring
variational autoencoder neural networks
correlation
limited sensors
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Identifying the structural state without baseline data is an important engineering problem in the field of structural health monitoring, which is crucial for assessing the safety condition of structures. In the context of limited accelerometers available, this paper proposes a correlation-based damage identification method using Variational Autoencoder neural networks. The approach involves initially constructing a Variational Autoencoder network model for bridge damage detection, optimizing parameters such as loss functions and learning rates for the model, and ultimately utilizing response data from limited sensors for model training analysis to determine the structural state. The contribution of this paper lies in the ability to identify structural damage without baseline data using response data from a small number of sensors, reducing sensor costs and enhancing practical applications in engineering. The effectiveness of the proposed method is demonstrated through numerical simulations and experimental structures. The results show that the method can identify the location of damage under different damage conditions, exhibits strong robustness in detecting multiple damages, and further enhances the accuracy of identifying bridge structures.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24082616