Discussion of the Definition for Acoustic Emission Event in the Time Series of Concrete Damage Process

Wang, Y.; Ge, Liu; Zhang, T. T.; Zhou, Li

doi:10.1134/s1061830919020104

Cited by 7 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yoon et al 15 have studied AE signals in corroded reinforced concrete using WT and reported high-energy signals near the failure stage of damage which are composed of several short closely spaced events. A similar observation of AE signal transition from burst type to continuous type when failure approaches in concrete under compression is reported by Wang et al 16 Sagaidak and Elizarov 17 have concluded that crack growth decreases the dominant frequency in AE spectra for concrete under flexure. Wang et al 18 have used peak frequency as an AE feature to investigate the influence of aggregate size and stress level on AE in mortar specimens.…”

Section: Introductionsupporting

confidence: 81%

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

Burud

Kishen

2020

Structural Health Monitoring

View full text Add to dashboard Cite

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.

show abstract