Low-Frequency ultrasonic tomography of Corrosion-induced damage patterns on naturally corroded solid reinforcing bar rock bolts
Publication Name
Construction and Building Materials
Abstract
A combined non-destructive guided ultrasonic wave measuring approach and low-frequency ultrasonic tomography technique on cylindrical surfaces is developed to investigate corrosion-induced damage patterns in naturally corroded 60-year-old solid rebars. The main goal of low-frequency tomographic imaging simulations is for differential detection of general and localized pitting corrosions on cylindrical surfaces of solid steel bars. The modified damage probabilistic reconstruction algorithm for cylindrical surfaces is also adopted to simulate naturally general and localized pitting corrosion patterns. To increase the ray density or resolution of images, a regular array of accelerometer sensors with high-density spatial distributions around the cylindrical surface of steel bars is installed for a fixed piezocomposite actuator. As such, an efficient sensor deployment method can be applied with the trilateration technique to achieve effective coverage and connectivity properties between accelerometer sensors. Stochastic signal processing which is elicited from the Hilbert-Huang Transform (HHT) signal method is also employed to analysis low-frequency tomography time domain algorithms. Finally, optical surface profilometry approaches, and static state tensile testings, together with scanning electron microscopy (SEM) are adopted to confirm the effectiveness of non-destructive and tomography imaging techniques. The results show that the guided wave signals with excitation frequency of less than 10 kHz can be precisely employed to identify the general and localized pitting corrosion distributions on the cylindrical surfaces of steel bars and qualitatively reconstruct them using low-frequency ultrasonic tomography simulations. This research study also demonstrates that the low-frequency ultrasonic tomography simulations can be effectively applied in corrosion pattern uncertainty modelling.
Open Access Status
This publication is not available as open access
Volume
392
Article Number
131694
Funding Number
PRESS2021