Assessment of damage in hydraulic concrete by gray wolf optimization‐support vector machine model and hierarchical clustering analysis of acoustic emission

Abstract

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.