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Classification of impulse radar waveforms using neural networks

G Vrckovnik1, T Chung, C R Carter

  • 1Airborne Radar Section, Defence Research Establishment Ottawa, Ontario, Canada.

International Journal of Neural Systems
|March 1, 1994
PubMed
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Multilayer neural networks accurately classify impulse radar waveforms from asphalt-covered bridge decks. This method accounts for variations in deck structure and material, achieving high classification accuracies.

Area of Science:

  • Civil Engineering
  • Artificial Intelligence
  • Non-Destructive Testing

Background:

  • Bridge decks exhibit structural variations and material degradation affecting radar waveform reflection.
  • Accurate classification of impulse radar waveforms is crucial for bridge health monitoring.

Purpose of the Study:

  • To demonstrate the efficacy of multilayer neural networks for classifying impulse radar waveforms from asphalt-covered bridge decks.
  • To assess the impact of structural variability and material changes on waveform classification.

Main Methods:

  • Utilized multilayer neural networks trained with the backpropagation algorithm and radial basis functions.
  • Applied principal components analysis for dimensionality reduction of input data.

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Main Results:

  • Achieved classification accuracies ranging from 89.9% to 100% without dimensionality reduction.
  • Attained classification accuracies between 95.6% and 100% after applying principal components analysis.

Conclusions:

  • Multilayer neural networks effectively classify impulse radar waveforms from diverse bridge deck structures.
  • Dimensionality reduction techniques can enhance classification performance for bridge deck analysis.