## 1 Introduction
While these clustering-based methods demonstrate utility, most simplify crack classification into binary tension/shear categories, overlooking the prevalence of mixed-mode fractures where both mechanisms coexist. Hybrid K-means/GMM methodologies offer enhanced crack-type discrimination. [PERSON] et al. [15] implemented a two-stage framework analyzing AE data from concrete four-point bending tests: K-means clustering provided initial data preprocessing before feature refinement through GMM classification. Subsequent ensemble voting categorized AE sources into shear, tensile, and mixed-mode fractures, significantly reducing the misclassification of shear cracks observed when using raw parameters alone. In recent years, most scholars have combined clustered acoustic emission parameters with other deep learning models to propose a series of damage prediction models. Specifically, [PERSON] et al. [19] developed a clustering-based methodology for detecting joint damage in the Sydney Harbour Bridge using vibration monitoring data, significantly enhancingstructural damage detection robustness. [PERSON] et al. [20] utilized self-organizing maps (SOM) coupled with support vector machines (SVM) to classify cracks in steel-fiber reinforced concrete under uniaxial tension, while quantifying damage evolution across varying fiber contents through AE energy analysis. [PERSON] et al. [21] devised a continuous wavelet transform (CWT)-convolutional neural network (CNN) framework that autonomously extracts features from AE time-frequency representations for damage-type recognition in composites, with classification results demonstrating strong alignment with K-means clustering. [PERSON] et al. [22] integrated K-means++ with a Gaussian mixture model sequence (GMMSEQ, a modified version of the original GMM that accounts for the fact that AE signals are indexed by continuous timestamps) clustering, subsequently combining both outputs with SVM to achieve automated labeling of four damage signal modes in cementitious materials. [PERSON] et al. [16] proposed a semi-parametric clustering (SPC)-SVM hybrid method for AE signal classification, attaining over 95% accuracy in concrete crack characterization. However, two significant limitations persist in current research: First, studies have predominantly focused on components with relatively simple damage processes, such as simply supported beams or axially compressed columns. Consequently, the damage-to-signal mapping mechanisms in complex stress components like eccentrically compressed columns remain inadequately investigated. Second, existing methods rely heavily on manual feature engineering, which struggles to effectively characterize the dynamic evolution of damage. In this context, the integration of unsupervised clustering with supervised methods offers distinct advantages. This combined approach can leverage unlabeled signal data to explore intrinsic relationships between damage mechanisms and signal clusters, thereby enabling the establishment of supervised clustering models that link discrete micro-crack events to macroscopic damage evolution. This study investigates reinforced concrete RC columns under eccentric compression through loading tests. Load-displacement curves and acoustic emission signals were synchronously acquired to analyze the evolution of AE parameters throughout the damage progression. By integrating K-means clustering with Gaussian mixture models, the damage stages and crack types within the RC columns were classified based on multimodal AE characteristics, including amplitude, energy, rise angle, and average frequency. Furthermore, a random forest-based model was established for automated crack classification.