Damage detection using wavelet entropy of acoustic emission waveforms in concrete under flexure

Abstract

This work dives into the spectral realm of acoustic emission waveforms. The acoustic emission waveforms carry a footprint of source, its mechanism, and the information of the medium through which it travels. The idiosyncrasies of these waveforms cannot be visualized from the time-domain parameters. The complex fracture process of the heterogeneous composite, such as concrete, reflects in the spectral disorder of acoustic emission signals. The use of wavelet entropy is proposed to estimate the spectral disorder. To evaluate wavelet entropy, the relative energy distribution in frequency sub-bands is determined using the wavelet transform. The Shannon entropy formulation as a wavelet entropy is utilized for discriminating spatiotemporally distributed acoustic emission events according to their respective level of disorder. The possible twofold application of the wavelet entropy as a signal discriminator and a damage index is qualitatively demonstrated. The increase in the statistical variance of wavelet entropy distribution with the increase in stress level reveals the presence of multi-sources as well as multi-mechanistic fracture process.