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This study explores a novel single-station coda wave interferometry method for detecting damage zones. The approach uses ultrasonic measurements and machine learning, proving feasible for identifying subsurface velocity changes in scattering media.

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Area of Science:

  • Geophysics
  • Materials Science
  • Artificial Intelligence

Background:

  • Traditional coda wave interferometry relies on dual-station setups.
  • A single-station approach offers potential advantages in deployment and cost-effectiveness.
  • Assessing subsurface changes in highly scattering media remains a challenge.

Purpose of the Study:

  • To evaluate the feasibility of a single-station coda wave interferometry technique.
  • To investigate the use of ultrasonic measurements for detecting velocity changes.
  • To apply machine learning for analyzing reflected coda wave signals.

Main Methods:

  • Finite-difference simulations of wave propagation were employed.
  • Ultrasonic measurements were simulated to detect velocity variations up to 1.6m depth.
  • 1D convolutional neural networks were utilized for signal prediction and analysis.

Main Results:

  • The single-station method demonstrated feasibility in identifying damage zones.
  • The approach proved robust against variations in crack density, crack length, and attenuation.
  • The influence of noise and sensor width on detection was analyzed.

Conclusions:

  • The proposed single-station coda wave interferometry is a viable method for damage detection.
  • The workflow integrates machine learning for enhanced analysis.
  • The technique is transferable for defect detection in concrete structures.