## 1 Introduction  
G20 Mn5 QT cast steel is a type of low-carbon alloy steel specified in the European code EN 10293 [1]. With sufficient weldability, the static bearing performance of G20 Mn5 QT is quite similar to that of A572 (used in America), S335 (used in Europe), and Q345D (used in China). The mechanical properties of G20 Mn5 QT and related welds under different working conditions have been widely studied, such as adhesively bonded steel tubes [2], stress analysis of the fatigue behaviors of the girth butt weld within welded cast steel joints [3], and the environment effects of G20 Mn5 QT cast steel [4, 5, 6]. Meanwhile, G20 Mn5 QT is usually cast into complex geometrical components, and under cyclic loads such as waves and winds, such load-bearing members and related welds can incur fatigue damage. Unfortunately, the fatigue resistance performance of G20 Mn5 QT and the corresponding butt welds is unsatisfactory [7, 8]; the reasons for this may include the large stress concentrations and defects introduced during the casting and welding processes. Recently, some investigations have been performed to reveal the characteristics of G20 Mn5 QT under cyclic loads. The strain-life curves of G20 Mn5 QT and related welds with Q345D were obtained by using uniaxial smooth bar specimens, and the Manson-Coffin model was proposed to evaluate the fatigue life [8]. [PERSON] et al. analyzed the influence of corrosion on the fatigue properties of G20 Mn5 QT cast steel and Q355 steel butt welds in 3.5-wt% NaCl solution and undergoing a dry-wet cycle [9, 10]. [PERSON] et al. investigated the fatigue performance and fatigue life prediction method of G20 Mn5 QT cast steel notches [11]. However, no research discussing the mechanisms of fatigue crack growth in G20 Mn5 QT and the corresponding welds has been reported. Acoustic emission (AE) technology is an effective method of non-destructive monitoring to evaluate material damage [12; 13; 14]. AE sensors collect stress waves generated due to the rapid energy release during the deformation of fracture sources and transform them into electrical signals. By analyzing the AE signals, information regarding the mechanism of the AE source can be obtained. Several studies have attempted to investigate the AE characteristics during the fatigue crack growth process in metal materials. The AE signals exhibited different responses in different fatigue crack growth stages [15; 16; 17], and the cracks' stable growth stage can be further divided into two sub-stages based on AE activity [18; 19]. Meanwhile, the welds demonstrated greater AE activity and more complex mechanism of AE sources compared to those of the base metal [17; 20; 21; 22]. It was found that both the AE hit release rate and AE energy release rate exhibited positive correlations with fatigue crack growth rates [23; 24; 25]; this phenomenon could be employed to predict fatigue residual life. Considering the irregularity of AE behaviors, an artificial neural network [26; 27] has been introduced into the fatigue crack growth estimation process to improve the accuracy. Moreover, AE could also be used in structural health monitoring. For example, [PERSON] et al. [28] monitored fatigue cracks on the eye-bars of the Alexandra bridge, which is a 587 m long steel truss bridge located on the boundary between Ottawa and Gatineau, Canada. The results showed that AE is a very reliable technology for confirming or denying fatigue cracks initiation and assessing the condition of existing cracks, and it is a cost-effective component in structural health monitoring. In addition, lots of scholars have employed AE to monitor structure damage and fatigue cracks in pressure vessels [29], cables and chains [30], tower structures [31], rail tracks [32], and welded joints [21]. AE monitoring technology has not only been found applicable for metal materials such as aluminium alloy (the PLC effect) [25], titanium alloy [33], steel, and related welds [17] but also for concrete [34] and composite materials [35].