Classification of damage in self-consolidating rubberized concrete using acoustic emission intensity analysis.

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2019
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Abstract
This article presents the results from an experimental study on the application of acoustic emission (AE) analysis to assess the damage progression of self-consolidating rubberized concrete (SCRC) mixtures under four-point flexural tests. Self-consolidating concrete (SCC) mixtures incorporating four variable replacements of crumb rubber (CR) including 0, 10, 20, and 30% were examined to study the effect of CR content on the AE damage detection and quantification capability. Normal concrete (NC) and normal rubberized concrete (NRC) mixtures, with 0 and 40% CR respectively, were also tested for the comparison. Two prism samples were tested from each mixture under monotonic loading conditions, while being constantly monitored via two attached AE sensors until failure. The acoustic emissions obtained from these sensors were related to the damage evolution of the tested prisms from all mixtures. The effects of variable mixture types and CR content on different AE parameters were highlighted. The considered AE parameters included signal characteristics (in terms of signal amplitude) as well as number of AE hits and cumulative signal strength (CSS). In addition, AE b-value and intensity analyses were further performed on the amplitude and signal strength of all AE signals to evaluate the extent of damage by means of three additional AE parameters including b-value, historic index, H (t), and severity, S. The outcomes of the AE analysis indicated the usefulness of the considered AE parameters in the detection of two early damage stages including micro- and macro-cracking before failure, irrespective of the CR content. Eventually, the intensity analysis parameters (H (t) and S) were correlated to the stages of micro- and macro-cracking in all mixtures for the purpose of damage classification via developed chart.
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abouhussien2019classificationultrasonics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Abouhussien, Ahmed A;Hassan, Assem A A;
Journal ultrasonics
Year 2019
DOI S0041-624X(19)30111-8
URL
Keywords Keywords not found

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