## 1 Introduction
With the motivation of this issue, the authors [PERSON] and [PERSON] (2018, 2019, 2020) developed 3D-PCT for damage detection in concrete and reinforced concrete [22; 23; 24]. Differently from the previous studies, we used a new approach-based source localization algorithm that considered the medium's heterogeneity, corrected whole AE signals, recalculated signal parameters, and conducted different parameter analyses [25]. According to the results, it was seen that deterioration of AE signals causes to presume the AE activity having more shear effectiveness and the energy is a more deteriorated parameter than the amplitude. To make this correction, the attenuation properties of the material must first be known, and relationships must be established using these properties. In addition to these innovations, this paper proposes to correct AE signals for fiber-reinforced concrete, a highly heterogeneous material. To characterize signal features of such different types of fiber activities and to verify the effectiveness of the proposed correction method, unsupervised pattern recognition was used, which is also used in the sub-branches of structural engineering such as earthquake engineering, building safety, and performance evaluation, especially for structural health monitoring and damage assessment. Clustering has been chosen as the validation method in this study, as it gives accurate results about crack modes and AE activity types such as matrix cracking or fiber breakage [26; 27; 28; 29; 30; 31; 32; 33]. Accordingly, [PERSON] et al. (2019) used pattern recognition to cluster cracking modes in steel fiber concrete and cement-based composites [34]. There are also many studies on which parameters to use for clustering AE signals. For example, in carbon fiber-reinforced polymer, any relationship was not observed between damage mechanisms and AE energy [35]. The literature also mentioned that the amplitude of the signal (related to the sensor location) is related to the magnitude of the activity rather than its type [30]. However, the frequency characteristics of the signal show a significant relationship with the damage type [36]. Studies show that matrix cracking can be differentiated at low frequencies and fiber breakage at high frequencies. Again, in these studies, instead of clustering based on the single frequency value of the signal such as center, average, or peak, it was observed that using weighted peak frequency (WPF), which is calculated as the geometric mean of the center and peak frequencies, is healthier and the frequency characteristics of the signal are better revealed. The parameter in which WPF is evaluated together in clustering is also suggested as partial power (PP). PP is defined as the percentage contribution of the signal to a specific frequency range in the frequency spectrum, and multiple PP can be calculated for different frequency ranges [37]. [PERSON] et al. (2012), [PERSON] (2016), [PERSON] et al. (2018), [PERSON] et al. (2018), [PERSON] et al. (2019), and [PERSON] et al. (2019) used WPF-PP relations for clustering AE activities [26; 37; 38; 39; 40; 41; 42]. For this reason, contributing to the existing literature, this paper proposes to identify how the elastic wave is affected by the type and amount of fiber in concrete and to correct AE signals for fiber-reinforced concrete. For this, first, attenuation properties of concrete samples, including different types and amounts of fibers were revealed by 3D-PCT using the change in the main AE parameter \"amplitude\". Here, acoustic pulses having different amplitudes with the same frequency and wave shape generated by a signal generator were sent to the points in the grids created on all samples. Colors were assigned to amplitude values recorded from each point, and 3D attenuation maps were created. Then, the samples were tested under flexure, and AE results were corrected using information from 3D contour maps and compared with mechanical findings.