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Updated: Nov 25, 2025

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Damage Identification in Plate Structures Using Sparse Regularization Based Electromechanical Impedance Technique.

Xingyu Fan1, Jun Li1,2

  • 1Guangzhou University-Curtin University Joint Research Centre for Structural Monitoring and Protection against Multi-Dynamic Hazards, School of Civil Engineering, Guangzhou University, Guangzhou 510006, China.

Sensors (Basel, Switzerland)
|December 16, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new sparse regularization method for quantifying structural damage using electromechanical impedance (EMI). The technique accurately detects minor damage in plate structures with a single piezoelectric transducer, outperforming traditional methods.

Keywords:
damage quantificationimpedanceplate structuresresonance frequency shiftssparse regularizationundetermined inverse problem

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Area of Science:

  • Structural Health Monitoring
  • Materials Science
  • Mechanical Engineering

Background:

  • Electromechanical impedance (EMI) techniques are widely used for structural damage detection.
  • Model-based EMI methods face limitations in complex or large-scale structures.
  • Accurate quantification of minor structural damage is crucial for maintaining structural integrity.

Purpose of the Study:

  • To propose a novel structural damage quantification approach using sparse regularization with the electromechanical impedance (EMI) technique.
  • To overcome the limitations of existing EMI methods for complex structures.
  • To accurately quantify minor structural damage in plate structures using a single piezoelectric transducer.

Main Methods:

  • Development and calibration of a 3D finite element model for PZT-structure interaction.
  • Application of sparse regularization based on resonance frequency shifts of impedance responses.
  • Inverse identification of structural damage using measured and analytically obtained impedance responses.
  • Experimental validation on an aluminum plate structure under varying temperature conditions.

Main Results:

  • The proposed sparse regularization EMI technique effectively quantifies structural damage.
  • The method demonstrates superior performance compared to conventional Tikhonov regularization.
  • Experimental results confirm the accuracy and effectiveness of the approach.
  • The technique shows robustness under varying temperature conditions.

Conclusions:

  • Sparse regularization offers a powerful approach for structural damage quantification via EMI.
  • The proposed method enhances the capability of EMI techniques for complex structural health monitoring.
  • This technique provides a reliable and accurate means for detecting and quantifying minor structural damage.