Detection of gaps in concrete–metal composite structures based on the feature extraction method using piezoelectric transducers

Paritosh Giri*, Spandan Mishra, Simon Martin Clark, Bijan Samali

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
25 Downloads (Pure)


A feature extraction methodology based on lamb waves is developed for the non-invasive detection and prediction of the gap in concrete–metal composite structures, such as concrete-filled steel tubes. A popular feature extraction method, partial least squares regression, is utilised to predict the gaps. The data is collected using the piezoelectric transducers attached to the external surface of the metal of the composite structure. A piezoelectric actuator generates a sine burst signal, which propagates along the metal and is received by a piezoelectric sensor. The partial least squares regression is performed on the raw sensor signal to extract features and to determine the relationship between the signal and the gap size, which is then used to predict the gaps. The applicability of the developed system is tested on two concrete-metal composite specimens. The first specimen consisted of an aluminium plate and the second specimen consisted of a steel plate. This technique is able to detect and predict gaps as low as 0.1 mm. The results demonstrate the applicability of this technique for the gap and debonding detection in concrete-filled steel tubes, which are critical in determining the degree of composite action between concrete and metal.

Original languageEnglish
Article number1769
Pages (from-to)1-11
Number of pages11
Issue number8
Publication statusPublished - 13 Apr 2019

Bibliographical note

Copyright 2019 by the authors. Licensee MDPI, Basel, Switzerland. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • concrete-filled steel tubes
  • debonding
  • feature extraction
  • gaps
  • partial least square regression
  • piezoelectric transducers
  • structural health monitoring


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