## 1 Introduction
The accuracy of the information that the AE method provides depends on the AE parameters and, thus, on factors such as the test setup, sensor, and material features. Unlike active NDT methods, energy sources cannot be controlled during the inspection of structural defects in AE and other passive NDT methods. Even though test conditions may be specified and repeatable, AE signals are random and can be influenced by unpredictable stochastic factors [23]. These factors can lead to variability in the AE dataset even when testing similar members and damages, especially damage source variables that affect the method's probability of detection (PoD). The POD approach is the cornerstone of the reliability evaluations of NDT methods, primarily because of its capacity to incorporate the criticality of defects [24; 25]. PoD is based on a binary method, i.e., true or false. Moreover, a statistical prediction is required for a large number of unknown flaws to evaluate the relevant factors of detectability [26; 27]. Generally, flaw size is used as a characteristic quantity. However, it is not possible to identify the flaw size in passive NDT methods. To utilize the PoD analysis on passive NDT outputs for infrastructure damage monitoring, the concept of curve generation for PoD, which investigates the inspection ability to detect defects in different industrial sectors, is a useful solution [28; 29; 30]. This curve typically relates the probability of damage detection to a characteristic parameter of the flaw, frequently its size [31]. While the PoD value defines the detection rate for a certain number of inputs, statistical distributions are utilized in real scenarios where the extent of damage is unknown, and signal response is evaluated for the AE dataset. The theory of PoD in AE was first introduced by [PERSON] in 2007 [PERSON] (2007), in which he developed a PoD curve to analyze the attenuation effects in a pressure vessel subjected to fatigue. [PERSON]'s study revealed that the PoD value decreases as the attenuation increases, particularly as the distance increases between the sensor and the damage. Building upon [PERSON]'s work, subsequent studies have further explored the application of PoD in various contexts. [PERSON] et al. (2018) investigated the PoD associated with source localization of pencil lead breakages acting as artificial AE sources in wood, whereas similarly, [PERSON] et al. (2018) examined the reduction in PoD in fiber composite plates in relation to sensor-to-damage distance. Since another significant factor influencing the detection probability of the AE method is the noise ([PERSON] et al., 2018), [PERSON] et al. (2018) observed a decrease in the PoD on the aluminum plate with increasing noise levels. Subsequently, [PERSON] et al. (2018) extended this understanding by analyzing AE monitoring of steel fatigue under cyclic loading. They found that increasing the threshold to filter noise resulted in decreased detection probability, with variations in PoD depending on the material type. However, it is crucial to note the caution raised concerning the threshold setting by [PERSON] and [PERSON] (2018). They highlighted that excessively reducing the threshold may lead to the detection of noise\(-\)related signals, such as secondary emissions, friction emissions, and signal reflections, which ultimately diminishes the PoD of AE. In summary, these studies collectively demonstrate the interplay between attenuation, sensor-to-damage distance, noise levels, threshold settings, and material properties in determining the PoD in AE applications. Nevertheless, while there are a limited number of studies in the literature investigating the PoD of damage detection using the AE technique in steel, wood, and fiber composites, there is still a huge gap to reveal how the detectability of AE signals is affected in concrete, a material that constitutes a significant part of the building stock.