## 1 Introduction  
Composite materials are increasingly used in lightweight transportation systems and civil engineering due to increasing weight constraints and installation costs. In the case of transportation systems such as in the aerospace and automobile industries, the use of composite materials reduces the weight, which is reflected in increasing the transportable load and reducing fuel consumption, and therefore the mechanical performance of the material is a very important and desired attribute. The use of fiber-reinforced polymer (FRP) composites is somewhat limited due to the possibility of sudden damage under load. Damage mechanisms in FRP are heterogeneous in that they often begin in-depth and then propagate at different length scales (micro-m meso-, or macroscales). In addition, the composite constituents (fiber, matrix, and interfacial bonding), and the applied mechanical load (stress level of loading sequence, stress ratio) influence the damage evolution [1]. This aggravates the development of damage-tolerant design procedures like those used for metallic materials. In polymer composites, this is mostly related to the development of experimental techniques that can monitor material behavior and offer real-time information about damage evolution [2]. Acoustic emission (AE) monitoring is one of the most suitable techniques to detect damage occurrence and its evolution in real-time during loading of FRP. Acquired AE signals during loading are a consequence of energy release from different damage modes during loading in the form of elastic waves and can be used for real-time insight into the construction part [3]. The sensitivity of AE monitoring can offer detection of different damage sources like matrix cracking, fiber failure, delamination, debonding between matrix and fibers, fiber pull-out, interfacial debonding, and friction [4; 5]. The most frequently reported damage modes of fiber composites are transverse matrix cracking, delamination, and fiber failure [6]. Damage mechanism characterization is based on analysis of acquired AE signals and prior knowledge of the nature of the damage source developed within the stressed specimen. Matrix cracking is usually connected with low amplitude signals between 40 and 60 dB, at low energy and frequency, while fiber-matrix debonding occurs between 45 and 70 dB in the medium frequency range [7]. The fiber breakage (failure) is usually associated with the highest amplitudes of 70-100 dB, at high energy and frequency, and usually occurs just before the complete failure of the specimen [8; 9]. Fiber cracking and pull-out and fiber-matrix friction have amplitudes between previous mechanisms and are harder to isolate from others [9; 10]. However, several studies have shown that AE signal amplitude is greatly affected by material properties, the geometry of the structure, and the acquisition system. More promising results for damage mechanism description were obtained by frequency content analysis of AE signals [11]. During composite loading, several damage mechanisms occur almost simultaneously, which creates a scientific challenge to assign a specific set of AE signal features to a particular damage mechanism. This is now most frequently solved with pattern recognition [12; 13]. [PERSON] et al. [8] used a sequential feature selection method based on a k-means clustering algorithm for the classification of AE signals in wind turbine blades loaded in the flap-wise direction. The visualization of clusters in peak frequency\(-\)frequency centroid features is used to correlate the clustering results with failure modes. [PERSON] et al. [11] used the Hilbert-Huang transform to extract frequency descriptors based on intrinsic mode functions for k-means pattern recognition for unidirectional GFRP during three-point bending. [PERSON] et al. [14] compared the use of k-means, fuzzy C-means clustering, and [PERSON]'s self-organizing map for discrimination of several failure modes in GFRP.