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Modelling of the electromechanical impedance technique for prediction of elastic modulus of structural adhesives

Author(s):



Medium: journal article
Language(s): English
Published in: Structural Health Monitoring, , n. 5, v. 20
Page(s): 147592172091692
DOI: 10.1177/1475921720916924
Abstract:

In order to strengthen and repair existing concrete structural elements, fibre-reinforced polymer composites are often externally bonded using structural adhesives. It is thus desirable to monitor the in situ performance of the sandwiched adhesive layer in such fibre-reinforced polymer–strengthened systems via its stiffness and strength gain throughout the curing process. The electromechanical impedance technique, which relies upon the utilisation of piezoelectric sensors, offers this capability. Although the technique has been verified experimentally in the laboratory, no known electromechanical impedance–based modelling study has been reported. This study, therefore, proposes the first electromechanical impedance–based finite element and analytical models to monitor the curing of structural adhesives. The dynamic elastic modulus of structural adhesives during curing can be determined from the developed models via a model updating process. Semi-empirical relationships were then developed to determine the tensile strength of structural adhesives from the resonance frequency obtained from the electromechanical impedance technique. This was made possible by correlation between static tensile tests on structural adhesives and the dynamic elastic modulus. These electromechanical impedance–based models were found to perform equally well when compared to the previously developed wave propagation–based models. This study shows the robustness of the electromechanical impedance technique for non-destructively predicting the dynamic elastic modulus and tensile strength of adhesives throughout the curing process.

Structurae cannot make the full text of this publication available at this time. The full text can be accessed through the publisher via the DOI: 10.1177/1475921720916924.
  • About this
    data sheet
  • Reference-ID
    10562516
  • Published on:
    11/02/2021
  • Last updated on:
    10/12/2022
 
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