Analysis of Concrete Axial Tensile Damage Degree Based on Acoustic Emission Signal Clustering Analysis

Abstract

To investigate tensile damage characteristics and mechanisms in depth, this study employed acoustic emission (AE) technology for real-time monitoring of the concrete axial tension process and integrated k-means clustering analysis to achieve self-organized classification of the collected AE signals. By applying correlation analysis of AE feature parameters and the coefficient of variation to characterize data dispersion, the optimal clustering results were selected. These results, combined with temporal analysis dividing the damage stages, collectively achieved the classification of concrete damage severity. Finally, through analyzing the underlying AE feature parameters within the clustering results, an effective interpretation of the concrete axial tension damage mechanisms at different severity levels was accomplished. This research method provides new insights into nondestructive testing and structural health monitoring of concrete structures, offering potential value for engineering applications. In the future, the method can be extended to more complex structural systems, and its applicability in long-term service environments can be explored to advance the development of intelligent monitoring and prediction technologies.