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

Abstract

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.