## 1 Introduction  
For engineering structures in service, safety and trustworthiness are of the utmost concern. In the numerous different operation modes that engineering structures are exposed to, there exist various types of possible failure mechanisms. In metals, common damage cases include fatigue, static failure, and friction each of which may result depending on the loading and working conditions to which the metal structures are exposed. The number of fatigue-prone structures in current and future engineering applications continues to grow, yielding the demand for a robust and efficient method of monitoring their structural integrity during operational use. The structural health monitoring (SHM) field is an emerging methodology that is used as a technique for detecting damages in structures, such as fatigue damage [1; 2; 3]. In general, the SHM approach involves the installation of sensors to collect pertinent data, enabling decisions on structure reliability and remaining life [4]. The SHM technique/process, in general, can be described in four levels. These levels explain how, from start to finish, an SHM system can yield useful information [5]. The four functional levels are as follows:* Level 1: Detection of the occurrence of an event;
* Level 2: Identification of the geometric location of the event, also known as \"source localization\";
* Level 3: Determination of the type, magnitude, and/or severity of the event;
* Level 4: Prognosis (estimation of remaining service utility/strength/service life). In this paper, a novel approach to discerning the length of a fatigue crack in metallic structures, an aspect of the aforementioned Level 3, is presented, utilizing artificial intelligence and the acoustic emission technique. The acoustic emission (AE) method, a passive SHM method, has been used and has seen significant growth in recent years as a method of monitoring damages, such as growing fatigue cracks. This AE method is well-established in the structural health monitoring and non-destructive evaluation (NDE) communities, with continued research and development since its inception [6; 7; 8; 9; 10; 11]. A damage case common to metallic structures is the initiation and growth of fatigue cracks due to various operating conditions that expose them to cyclic loading, which in turn generates acoustic emission signals that can be analyzed. [PERSON] et al. [12] used excitation potentials to perform numerical and theoretical analyses of these guided waves released during an acoustic emission event. These analyses were used to predict the out-of-plane displacement of the AE guided wave at some distance away from the source, as well as provide trends in the characteristics of A0 and S0 modes in the waves. [PERSON] et al. [13] studied the analytical models of AE signals by using the modal analysis of Lamb wave propagation and the AE sensor frequency response to develop an algorithm for obtaining model AE signals that are very similar to the experimental signals. [PERSON] et al. [14] investigated the behavior of fatigue crack growth events by studying a single fatigue cycle. This study yielded an understanding that crack growth can occur during both loading and unloading so long as the strain energy release rate is greater than a crack growth threshold value. [PERSON] et al. [15; 16] developed a novel hybrid meshless displacement discontinuity method for cracked Reissner's plate. Much initial research [17; 18; 19; 20; 21] has been performed in recent years to utilize and adapt the AE methodology for fatigue and impact damage monitoring in composites, an area that poses a distinctive challenge due to the unique material characteristics of composite materials and their impact on wave propagation. While many researchers have used sensors to capture AE waves and have analyzed the wave characteristics, not much research has been performed to establish a relationship between the length of a fatigue crack and the AE waveform signatures. Some initial novel work from [PERSON] et al.