Contact and non-contact non-destructive detection of debonding of tiles
Access status:
Open Access
Type
ThesisThesis type
Doctor of PhilosophyAuthor/s
Zhao, YuAbstract
Debonding of tiles in high-rise buildings poses safety threats, making reliable non-destructive inspection (NDI) essential. This study systematically investigates contact and non-contact acoustic NDI methods for detecting tile debonding or loose fixings, elaborating on their ...
See moreDebonding of tiles in high-rise buildings poses safety threats, making reliable non-destructive inspection (NDI) essential. This study systematically investigates contact and non-contact acoustic NDI methods for detecting tile debonding or loose fixings, elaborating on their mechanisms and procedures. It also provides algorithms applicable to practice, such as identifying loose slot wedges in hydropower generator stators. First, a non-contact acoustic method using a directional sound source and Laser Doppler Vibrometer (LDV) is established. A numerical model simulates acoustic interaction with tiles. The debonding area is identified by plotting the out-of-surface velocity map based on vibration amplitudes at resonance frequency. Numerical simulations agree with experiments, confirming accuracy for various debonding shapes. To enhance efficiency, a deep learning (DL) method is proposed. Continuous wavelet transform converts signals into time-frequency scalograms to build a signature database. Two DL networks are trained: the first classifies debonding types (100% accuracy with a single scalogram), and the second identifies unknown shapes (errors 1–31%). This two-stage method offers a fast, effective solution. The non-contact approach is extended to inspect slot wedges in hydropower generators. Wedges are classified as loose, intermediate, or normal. Using a directional sound source and analyzing frequency peaks (900–2100 Hz), loose and normal conditions achieve 100% accuracy; with DL, all three reach 100%. This method reduces operation time compared to contact techniques. Finally, a contact inspection method using a hammer and microphone mimics human hearing. Debonded areas produce distinct acoustic waveforms. A Fast Fourier Transform-based approach identifies resonance frequencies, and the Digital Damage Fingerprints method maps debonding. This eliminates subjectivity inherent in worker-dependent methods, yielding more reliable results
See less
See moreDebonding of tiles in high-rise buildings poses safety threats, making reliable non-destructive inspection (NDI) essential. This study systematically investigates contact and non-contact acoustic NDI methods for detecting tile debonding or loose fixings, elaborating on their mechanisms and procedures. It also provides algorithms applicable to practice, such as identifying loose slot wedges in hydropower generator stators. First, a non-contact acoustic method using a directional sound source and Laser Doppler Vibrometer (LDV) is established. A numerical model simulates acoustic interaction with tiles. The debonding area is identified by plotting the out-of-surface velocity map based on vibration amplitudes at resonance frequency. Numerical simulations agree with experiments, confirming accuracy for various debonding shapes. To enhance efficiency, a deep learning (DL) method is proposed. Continuous wavelet transform converts signals into time-frequency scalograms to build a signature database. Two DL networks are trained: the first classifies debonding types (100% accuracy with a single scalogram), and the second identifies unknown shapes (errors 1–31%). This two-stage method offers a fast, effective solution. The non-contact approach is extended to inspect slot wedges in hydropower generators. Wedges are classified as loose, intermediate, or normal. Using a directional sound source and analyzing frequency peaks (900–2100 Hz), loose and normal conditions achieve 100% accuracy; with DL, all three reach 100%. This method reduces operation time compared to contact techniques. Finally, a contact inspection method using a hammer and microphone mimics human hearing. Debonded areas produce distinct acoustic waveforms. A Fast Fourier Transform-based approach identifies resonance frequencies, and the Digital Damage Fingerprints method maps debonding. This eliminates subjectivity inherent in worker-dependent methods, yielding more reliable results
See less
Date
2026Rights statement
The author retains copyright of this thesis. It may only be used for the purposes of research and study. It must not be used for any other purposes and may not be transmitted or shared with others without prior permission.Faculty/School
Faculty of Engineering, School of Aerospace Mechanical and Mechatronic EngineeringAwarding institution
The University of SydneyShare