Predictive modeling of electromechanical impedance spectroscopy for composite materials

Matthieu Gresil, Lingyu Yu, Victor Giurgiutiu, Michael Sutton

Research output: Contribution to journalArticleResearchpeer-review

54 Citations (Scopus)


The advancement of composite materials in aircraft structures has led to an increased need for effective structural health monitoring technologies that are able to detect and assess damage present in composite structures. The study presented in this article is interested in understanding self-sensing piezoelectric wafer sensors to conduct electromechanical impedance spectroscopy in glass fiber reinforced polymer composite to perform structural health monitoring. For this objective, multi-physics-based finite element method is used to model the electromechanical behavior of a free piezoelectric wafer active sensor and its interaction with the host structure on which it is bonded. The multi-physics-based modeling permits the input and output variables to be expressed directly in electric terms, while the two-way electromechanical conversion is done internally in the multi-physics-based finite element method formulation. The impedance responses are also studied in conditions when the sensor bonding layer is subject to degradation and when the sensor itself is subjected to breakage, respectively. To reach the goal of using the electromechanical impedance spectroscopy approach to detect damage, several damage models are generated on simplified orthotropic structure and laminated glass fiber reinforced polymer structures. The effects of the modeling are carefully studied through experimental validation. A good match has been observed for low and high frequencies.

Original languageEnglish
Pages (from-to)671-683
Number of pages13
JournalStructural Health Monitoring
Issue number6
Publication statusPublished - 1 Nov 2012
Externally publishedYes


  • composite materials
  • Electromechanical impedance spectroscopy
  • finite element modeling
  • piezoelectric wafer active sensors
  • structural health monitoring

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