COA-Optimized Kernel K-Means Clustering for Identifying Acoustic Emission Signals Associated with Different Damage Types in RC Beams

Acoustic emission (AE) signals associated with different damage processes in reinforced concrete (RC) beams often show overlapping feature distributions, making unsupervised identification difficult. In this study, three RC beams were tested under loading-induced damage, freeze–thaw damage and reinforcement corrosion conditions, and 490 valid AE samples were obtained. Seven AE parameters were selected to construct the clustering feature set. To enhance the separation of different damage-related AE signals, a kernel K-means clustering framework was adopted, and the coyote optimization algorithm was used to optimize the kernel function type and key parameters based on the Gap Statistic. Comparative analysis with K-means, FCM and GMM was also conducted. The results show that the COA-optimized kernel K-means method achieved the best overall clustering performance, increasing the mean ACC by 10.35 percentage points compared with K-means and by 2.49 percentage points compared with GMM. Its mean ARI was also higher than that of GMM by 0.0330, while the standard deviation of ACC decreased from 7.73% to 4.19%. Class-level results indicated that loading-induced AE signals were more readily identified, whereas freeze–thaw and corrosion signals were more affected by feature overlap. Feature interpretation further showed that the main misclassified samples were located in transitional feature regions. The results suggest that COA-based kernel optimization can improve the clustering separation and stability of AE signal identification for different damage types in RC beams.