Summary Damage and failure of rubberized self‐compacting concrete (RSCC) under uniaxial tension are investigated by acoustic emission (AE) and digital image correlation (DIC) techniques. Four RSCC mixtures containing fine rubber particles with 0%, 5%, 10%, and 15% volume fractions are tested. The effect of rubber content on the macroscopic mechanical behavior, the AE parameters, the strain fields, and the damage developments are analyzed. It is demonstrated that the combined use of AE parameters and DIC strain maps provides an accurate estimation of different stages of damage evolution and that the crack propagation measured by DIC correlates strongly with all AE parameters. Modes of cracking are determined by analyses of average frequency (AF) versus rise time–amplitude (RA) values to demonstrate an additional feature of AE, which can be used for explanations of different behaviors under different loading conditions. It is shown that the substitution of fine aggregates with fine rubber particles leads to a reduction of stiffness, strength, and fracture toughness when the material experiences uniaxial tension. This has to be considered in the design of structures with RSCC.

Summary Acoustic emission (AE) is a useful method for recording fracture processes in concrete. In this work, AE data are recorded during three‐point bending tests to fracture of hydraulic concrete. First, AE data are used to analyze concrete's damage development using hits distribution, b‐value, Ib‐value, and average frequency versus RA value. Second, clustering analysis of AE signals is performed by hierarchical clustering. Third, a support vector machine model based on the gray wolf optimization algorithm is proposed to quantify the degree of damage. Via b‐value analysis it is shown that the fracture process of hydraulic concrete can be divided into three stages: microcracks nucleation; microcracks coalescence into macrocracks (macrocrack nucleation); and macrocrack propagation. Further, it is shown that rise time, ringdown counts, energy, duration, amplitude, and central frequency can be used to characterize the failure modes. Specifically, it is found that microcrack nucleation stage is dominated by tensile failures, macrocrack nucleation stage is characterized by rapid increase of shear failures, which become dominant over tensile failures, and macrocrack propagation stage is dominated by shear failures. Via hierarchical cluster analysis, it is found that the fracture process can be divided into three clusters, which corresponds to the three stages obtained via b‐value analysis. Finally, the proposed support vector machine model based on gray wolf optimization is found to predict the degree of damage in excellent agreement with experiment. This offers an effective practical method for damage assessment by combining AE with machine learning.

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