Abstract
Shotcrete structures are widely used in tunnel engineering. Quality inspection is difficult, and the traditional ultrasonic testing (UT) method based on first arrival velocity has limitations. In this paper, shotcrete-rock specimens were made in a laboratory and evaluated using UT. Wavelet packet decomposition is introduced for better frequency analysis of the condition evaluation. Two methods, including calculation of the energy eigenvalues and machine learning, are used to describe the contact quality at the interface between the shotcrete and rock. The relative energy eigenvalue increases with the gradual reduction of contact quality, which can become a quantitative index of the contact quality. Machine learning performed well in the rapid recognition of discontinuities in the multiple-classification models. Both methods based on wavelet packet decomposition achieved good results in identifying discontinuities and have the potential to be used in practical engineering applications.
Publisher
The American Society for Nondestructive Testing, Inc.
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science