## 5 Conclusions  
This study investigates the acoustic emission characteristics of reinforced concrete columns under eccentric compression. AE data were classified using K-means clustering integrated with GMM. Damage labels were assigned by correlating clustering results with experimentally observed failure stages. A random forest model was subsequently trained on AE feature parameters for damage prediction, yielding the following conclusions:  
1. AE signal characteristics exhibit strong correlation with damage progression in RC columns. Variations in AE signatures effectively identify damage stages, while eccentricity significantly influences both failure mechanisms and corresponding AE patterns.
2. The damage stages of reinforced concrete eccentrically compressed columns are divided into four stages using acoustic emission signals and experiments. Different stages have different acoustic emission signal patterns with varying amplitudes and energy ranges. As the damage stage changes, the amplitude and energy range of the acoustic emission further increases.
3. K-means clustering partitioned AE data into four clusters. However, high-dimensional AE parameter space limits cluster differentiation. Therefore, cluster interpretation incorporated GMM-processed RA-AF results and experimental observations, establishing seven distinct damage labels for classification, which provides a more comprehensive and detailed overview of the different types of damage that occur.  
Figure 17: Predicting results by random forest model: (**a**) confusion matrix for predicting results; (**b**) evaluation indices for projected results.  
(4) The integrated K-means/GMM supervised learning model achieved an 85% overall accuracy in identifying damage states. Nevertheless, predictive performance warrants further refinement through enhancements such as temporal feature optimization.  
These findings demonstrate that AE monitoring combined with machine learning algorithms can quantitatively characterize concrete damage mechanisms under eccentric loading conditions. The classification model proposed in the experiment can be used to judge the damage type of eccentrically compressed concrete columns, such as the structural health monitoring of columns in bridges or frame structures. Due to limitations in experimental conditions and the limited amount of data for Type 3, it was not accurately predicted. In the future, research on the following aspects could be undertaken:  
Temperature, humidity, and other factors should be introduced into the model to improve its applicability;  
Combined with a time series, the classification model should be strengthened to predict the occurrence and location of the structural damage.  
Conceptualization, A.J. and T.-M.O.; methodology, A.J.; software, S.D.; investigation, A.K. and T.H.; resources, A.J.; data curation, S.D. and A.K.; writing--original draft preparation, S.D.; writing--review and editing, A.J.; visualization, S.D.; supervision, A.J.; project administration, A.J.; funding acquisition, A.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Xinjiang Uygur Autonomous Region (grant no. 2022D01C685), the National Natural Science Foundation of China (grant no. 52368051) and the Postgraduate Research Innovation Project of Xinjiang Uygur Autonomous Region (grant no. 107552301571).  
The data presented in this study are available upon request from the corresponding author.  
The funding sources and support are gratefully acknowledged.  
The authors declare no conflicts of interest.