Abstract
This article reports the characterization of generated AE during the tensile fracture process in Steel fibre reinforced concrete (SFRC) using Wavelet Packet Decomposition (WPD) and pattern recognition to study the damage development and post-peak response in SFRC. Split-Tensile tests were performed on plain concrete and SFRC specimen in the laboratory and the AE waveforms generated during the fracture were classified into two categories, namely (a) AE waveforms for cementitious matrix cracking and (b) AE waveforms generated due to steel fibre pullout, by implementing unsupervised learning and supervised learning techniques. It was observed that if there is active participation of steel fibres in crack bridging then the AE energy for fibre pullout is more. As the volume fraction of the steel fibres decreases, the number of AE hits corresponding to steel fibre pullout decreases. Classification of the AE waveform may be useful to understand the development of damage during the tensile fracture process in SFRC.
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