Wavelet entropy-based damage characterization and material phase differentiation in concrete using acoustic emissions

Damage evolution in concrete is a complex phenomenon involving micro -cracks that originate from different material phases (mortar matrix, ITZ and aggregate), and culminate into a major crack. The information about the individual damaged material phases can provide major insights into the global failure behaviour, cracking path, and fracture processes. The evolution of damage, damage mechanisms and the material phase of origin of micro -cracks is highly influenced by the type of concrete (Normal and high strength). In order to investigate the evolution of damage in concrete of varying strength, notched beam specimens with different water -to -cement ratios have been prepared and tested under CMOD control. The evolution and growth of damage have been continuously monitored during testing through acoustic emission techniques. Based on acoustic emission results, it has been demonstrated that the damage progression in concrete can be better described as a function of the wavelet entropy of the AE events occurring during the entire loading duration, and therefore, an analytical model for the damage evolution has been proposed. The study reveals that, under the high -strength category, the level of deterioration reduces as the w/c ratio rises, while in normal -strength concrete, a reverse trend is observed. Furthermore, a novel approach has been proposed that utilizes wavelet entropy and amplitude to differentiate amongst various damaged material phases with the help of an unsupervised clustering algorithm. Normal -strength concrete with a w/c ratio of 0.4 has been used to demonstrate the proposed methodology and has been validated with the help of experiments performed on the mortar and the parent stone of aggregate. A higher occurrence of mode -I cracks compared to mode -II cracks across all material phases has been observed for the specimen. Additionally, ITZ and aggregate exhibit a greater proportion of tensile and shear cracking events than the matrix phase. In the end, a deep learning convolutional neural network (CNN) model is devised utilizing labelled scalogram images of the AE waveforms to discriminate between the various damaged material phases. A 10 -fold training cross -validation of the dataset has been carried out to achieve a stable performance. The deep learning model performs well in discriminating the damaged material phases with high training, validation, and testing accuracy.