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Application of Graph Convolutional Neural Networks Combined with Single-Model Decision-Making Fusion Neural Networks

Xiaofei Li1, Langxing Xu1, Hainan Guo2

  • 1College of Transportation Engineering, Dalian Maritime University, Dalian 116026, China.

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|December 9, 2023
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Summary

This study introduces a novel single-model decision-making fusion neural network (S_DFNN) for structural damage identification. This advanced deep learning approach enhances accuracy by integrating data fusion with graph convolutional neural networks (GCNs).

Keywords:
damage identificationdecision-level data fusiongraph convolutional neural networksmodel testingsensor spatial characteristics

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Area of Science:

  • Structural health monitoring
  • Artificial intelligence in engineering
  • Signal processing

Background:

  • Accurate structural damage identification is crucial, especially with numerous sensors and complex spatial arrangements.
  • Traditional methods struggle with environmental interference and sensor instability affecting vibration signals.
  • Graph convolutional neural networks (GCNs) offer potential for learning sensor spatial characteristics.

Purpose of the Study:

  • To develop an improved structural damage identification method.
  • To enhance the accuracy and robustness of damage detection in various structures.
  • To address limitations of existing GCNs in real-world conditions.

Main Methods:

  • Proposed a single-model decision-making fusion neural network (S_DFNN).
  • Integrated data fusion technology into the decision-making layer of a GCN model.
  • Utilized high-performance graphical convolutional deep learning.

Main Results:

  • The S_DFNN model demonstrated superior damage recognition performance across different structures.
  • Accuracy was significantly improved compared to single-model approaches.
  • The method showed good damage identification effects and strong generalization ability.

Conclusions:

  • The proposed data fusion and deep learning method is effective for structural damage diagnosis.
  • The S_DFNN model exhibits robust performance and high accuracy in identifying structural damage.
  • This approach holds significant potential for practical applications in structural health monitoring.