## 1 Introduction  
Acoustic emission (AE) methods are a promising approach to monitoring the damage process in structural systems, and thus have found numerous applications in various engineering branches. Briefly, AE methods refer to the occurrence of micro-fractures within a structure inducing elastic waves, which propagate throughout the system and can be detected with suitable sensors, usually located on external surfaces [1]. In the present work, the waves emitted by the sources are called _events_, and the responses captured by the sensors are called _signals_. In this paper, the damage process in a cube of concrete is studied through AE methods. The cube is subjected to compression until it reaches failure, with global results presented in terms of the failure load and final configuration of the specimen. Two independent systems perform the corresponding AE data acquisition: eight sensors connected to Lunitek [2] and two AE sensors connected to Mistras [3] conditioning devices. The damage process is also simulated using a discrete element method (DEM) version. Information from this simulation and both acquisition systems support the investigation of damage evolution through different AE global parameters, such as the \(b\)-value, initially used by [PERSON] [4] and applied in several articles, most notably by [PERSON] et al. [5], and a natural time (NT) analysis was proposed by [PERSON] et al. [6]. In the following, we present the essential aspects of the primary analysis tools used here: the \(b\)-value and NT techniques for analyzing AE data and the DEM version used in the simulations, originally proposed by [7]. _b-value analysis_: this damage assessment criterion is provided by the statistical analysis of the amplitude distribution of AE signals generated by growing microcracks, as shown schematically in Figure 1. This distribution of amplitudes (\(A\)) conforms to the Gutenberg-Richter (GR) law [4], \(N(\geq A)\propto A^{-b}\), where \(N\) is the number of AE signals with amplitude \(\geq A\). The GR law's exponent \(b\), the so-called \(b\)-value, changes with the different stages of damage growth: while the initially dominant microcracking generates large numbers of low-amplitude AE signals, the subsequent macroracking generates fewer signals of higher amplitude. As a result, the \(b\)-value progressively reduces as the specimen approaches impending failure. [PERSON] et al. [8] offers an alternative interpretation of the \(b\)-value in connection with the so-called bundle model, extensively explored by [9], which consists of a set of parallel-connected bars with a random strength distribution that are progressively loaded until model failure is reached. The histogram of burst distributions (i.e., the number of simultaneously broken bars) and their magnitudes have a characteristic slope in the bi-logarithmic dominium, which matches the \(b\)-value in AE signals from physical systems. Thus, \(b\)-values calculated from a simulated model can be used as a precursor to critical stages in similar physical systems. The use of the \(b\)-value as a precursor to damage in AE tests is explored in [5; 10; 11; 12; 13; 14; 15; 16; 17; 18], among others. There are other global indexes based on the spatial and temporal distribution of AE events, as described in [19; 20; 21; 22]. Some of these parameters were also explored by the authors in previous works; see [23; 24]. _Natural time (NT) analysis_: This approach, proposed by [PERSON] et al. [25], is proven to be ideal for enhancing the characteristics of the studied signals, reducing uncertainties, and optimizing the extraction of information, especially in systems approaching criticality. Relevant examples of this method's usefulness appear in a large variety of fields. For specific applications in the analysis of AE signals, see [26; 27; 28; 29].