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Semi-Automated Air-Coupled Impact-Echo Method for Large-Scale Parkade Structure.

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Summary
This summary is machine-generated.

This study introduces an Artificial Neural Network (ANN) for faster structural health monitoring (SHM). The ANN efficiently detects damage in reinforced concrete structures using impact-echo data, reducing testing time.

Keywords:
artificial neural networkdamage detectionenergy impact factorimpact-echomachine learningreinforced concretewavelet transformation

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Area of Science:

  • Civil Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Structural Health Monitoring (SHM) systems are increasingly data-intensive, employing diverse sensors for infrastructure assessment.
  • Large-scale structures like bridges and dams present challenges in terms of inspection time and scale.
  • Automated measurement systems are crucial for timely data acquisition in SHM.

Purpose of the Study:

  • To propose an Artificial Neural Network (ANN) application for identifying intact and damaged locations in reinforced concrete structures.
  • To utilize impact-echo data collected from both laboratory and field experiments for damage detection.
  • To reduce the time required for in situ damage detection testing in civil infrastructure.

Main Methods:

  • Implementation of an Artificial Neural Network (ANN) for data analysis.
  • Collection of impact-echo data using air-coupled microphones from reinforced concrete beams.
  • Conducting lab experiments and a field experiment in a parking garage, with semi-autonomous impact-echo testing.
  • Employing a least-square distance approach for classification, enhanced by the ANN to eliminate user-defined cutoff values.

Main Results:

  • The ANN successfully determines intact and damaged locations from a small training sample of impact-echo data.
  • The semi-autonomous field testing expedited the initial damage detection process.
  • The ANN's ability to forgo user-defined cutoff values streamlines the classification of structural integrity.

Conclusions:

  • The proposed ANN application shows significant potential for reducing testing time in SHM of large-scale civil reinforced concrete structures.
  • Automated systems, like the developed ANN, are key to overcoming the challenges of scale and time in infrastructure monitoring.
  • This approach contributes to more efficient and effective in situ damage detection.