Wavelet and fractal analysis of acoustic emission characteristic of fatigue damage of asphalt mixtures

Fatigue cracking of asphalt materials directly affects the performance and safety of asphalt pavements. It is necessary to identify the materials condition with acoustic emission (AE) as the real time monitoring technique. The objective of this study was to diagnose the damage behavior of asphalt mixtures from the time and frequency characteristics of AE signals. Firstly, the semi-circular bending (SCB) fatigue test and AE detection were conducted to explore the AE response of asphalt mixtures subjected to cyclic loads. Then, the Fast Fourier Transform (FFT) and Continuous Wavelet Transform (CWT) were applied to discuss the dominant frequency characteristics and time-frequency distribution of AE waveforms. Finally, an index (R-BD), defined as the ratio of fractal dimension of AE waveforms decomposed by the Discrete Wavelet Transform (DWT), was proposed to evaluate the damage state of asphalt mixtures. The results show that AE energy rate exhibits distinct variation signatures at different damage stages. The average frequency (AF) and rise angle (RA) of AE signals reveal that the coupling effect of tensile and shear cracks promotes the deterioration of asphalt mixtures. Most of the dominant frequencies of AE events generated from cracks are concentrated within the range of 13 to 30 kHz. The discrepancies in the time-frequency distribution of AE events could effectively discriminate the types of damage sources. The proposed index RBD is sensitive to the change of damage state of asphalt mixtures, producing corresponding reductions.

Automated summary

The objective of this study was to diagnose the damage behavior of asphalt mixtures using acoustic emission (AE) as the real-time monitoring technique. The study found that the time and frequency characteristics of AE signals can reveal the damage state of asphalt mixtures, and proposed an index RBD to evaluate the degree of damage.