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Wave based damage detection in solid structures using spatially asymmetric encoder-decoder network.

Frank Wuttke1,2, Hao Lyu3,4, Amir S Sattari5

  • 1Geomechanics and Geotechnics Group, Kiel University, 24118, Kiel, Germany. frank.wuttke@ifg.uni-kiel.de.

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This study introduces a novel neural network model for structural damage detection. The spatially asymmetric encoder-decoder network effectively identifies crack initiation by recognizing changes in wave field patterns, offering a promising alternative to conventional methods.

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Area of Science:

  • Engineering
  • Artificial Intelligence
  • Materials Science

Background:

  • Structural damage identification is crucial for infrastructure longevity and public safety.
  • Conventional monitoring methods are often engineer-intensive and time-consuming.
  • There is a need for efficient and automated structural health monitoring (SHM) techniques.

Purpose of the Study:

  • To investigate the efficacy of neural networks in recognizing structural property alterations.
  • To develop and evaluate a novel encoder-decoder network for crack detection.
  • To assess the potential of wave field pattern recognition for SHM.

Main Methods:

  • Implementation of a spatially asymmetric encoder-decoder network utilizing 1D-Convolutional Neural Networks (CNNs).
  • Training the model using dynamic lattice model data to recognize wave field changes.
  • Focus on identifying wave field alterations post-crack initiation in structures.

Main Results:

  • The proposed neural network model demonstrates the ability to recognize wave field changes indicative of crack initiation.
  • Successful crack detection accuracy was achieved through the implemented training procedure.
  • The model shows potential for automated structural health monitoring.

Conclusions:

  • Neural networks, specifically the proposed encoder-decoder CNN, offer a viable approach for detecting structural damages.
  • This method has significant potential to enhance conventional crack detection and SHM strategies.
  • Further development could lead to more efficient and automated infrastructure monitoring.