Debonding Detection in CFST Structures Using Wavelet Packet Energy Ratios and Agglomerative Hierarchical Clustering

This study develops a framework combining wavelet packet analysis and agglomerative hierarchical clustering (AHC) for debonding identification in concrete-filled steel tubular (CFST) structures. An acoustic–structure coupling model was established and assessed against percussion measurements from an intact region of a CFST specimen. Numerical simulations examined the effects of void geometry on impact-acoustic responses and three wavelet packet indicators: the cumulative energy-ratio difference index, energy-ratio deviation, and energy-ratio variance. Experimental tests were conducted on a single CFST specimen containing voids simulated by expanded polyethylene (EPE) foam blocks, under quiet conditions, background noise, denoising, and variations in microphone and impact positions. Each experimental signal was represented by a 92-dimensional wavelet packet energy-ratio feature vector. AHC and six additional unsupervised clustering methods were evaluated using quantitative performance metrics. AHC correctly classified all void and non-void samples under every tested condition. The results support the feasibility of combining wavelet packet energy-ratio features with unsupervised clustering for distinguishing void and non-void regions in the single tested CFST specimen under the investigated laboratory conditions and EPE-foam simulated-defect configurations.