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.

Research on Application of Uplift-Restricted Slip-Permitted (URSP) Connectors in Steel-Concrete Composite Frames

“…In recent decades, steel-concrete composite structures, such as concrete-filled steel tubes (CFST) [1][2][3], steel-plate concrete beams [4][5][6], partially encased composite (PEC) columns [7][8][9][10], and steel-concrete composite frames [11,12], have received much attention [13][14][15]. Among them, the steel-concrete composite frame is currently a widespread structural system in multistory and high-rise buildings since it combines the advantages of both steel and concrete frames, thus leading to, in many cases, a reduction of costs and optimization of the structural performance [16,17].…”

Acoustic Inspection of Concrete Structures Using Active Weak Supervision and Visual Information

“…The compacity of defect and non-defect areas of the tested structure is expressed through the introduction of a spatial estimator, based on a spatial neighborhood NB(X i ) for each sample X i . This is defined using the position information of hammering samples, as in Equation (10). A spatial estimator h ij is then used to estimate the fuzzy membership coefficients of the considered sample based on its neighborhood, as in Equation (11), with |NB(X i )|, the number of neighbors for sample X i .…”

“…In [9], Ensemble Learning was used with time-frequency analysis. In [10], Partial Least Squares Regression was used to predict the gap in concrete-metal composite structures. While achieving high performance, such supervised learning approaches have an inherent dependency on the available training data.…”

Debonding Size Estimation in Reinforced Concrete Beams Using Guided Wave-Based Method

“…The bond-slip failure in a steel-concrete-steel sandwich structure was monitored nondestructively using wave propagation by Yan et al [27]. The partial least square regression (PLSR) technique was employed to detect the gaps in the steel-concrete composite structures by Giri et al [28]. Their method provided the use of a minimal number of sensors attached to the internal surfaces of the tested structures.…”