# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 3 Results and Discussion  
In recent years, there has been a consistent use of the N, NN, and RH as illustrated in Figure 12. Also, there is a steady significant use of the R/H methods. Figure 12: Trend in applications of machine learning to FL prediction (2013–2023). In the study where the peak stress and notch stress methodology of evaluating FL were compared. The experimental data included a variation in terms of thickness and stress conditions for a different variety of welded joint configurations to calculate the notch stress intensity factors (NSIF). Although, both techniques yielded a regression line that was shallower than the design reference curve as depicted in Figure 13a,b [22]. While in the work that varied monitoring data and traffic densities, there was generally a good agreement between the monitored-based performance indicator profiles and model simulations, the only drawback was that it was deterministic. When examining the unique features of fatigue failure for steel pipes with various strength levels, it was discovered that the stress range no longer influences the number of cycles once maximum tensile stress is reduced to 6-8%, However, regression equations that can be used to forecast the remaining life of a specific pipeline section were created [25]. Similarly, the number of required analyses to obtain SIF and FL in pipes would become less by about 20%, if the technique by [PERSON] was implemented [67]. In GMAW (Gas Metal Arc Welding) welded connections, it was deduced that longitudinal corrugated plates' welded joints' fatigue failure mechanisms are equivalent to those of the corrugated beams' fatigue failure whose welded features' fatigue strength can be effectively represented by a suggested simple test specimen [27]. In addition, GME has the benefit of quick calibration, and it is feasible to anticipate welded joint fatigue accurately in GMAW joints [33]. To determine the optimum rail replacement time for continuously joined rails on top of the concrete slab track, as demonstrated by the Korea High-Speed Railway, it is important to compute the fitting fracture probability considering diverse vehicles alongside track  
Figure 13: (**a**) Notch stress approach—equivalent stress range vs. number of cycles for pipe-to-plate and lap joints; (**b**) peak stress method—peak stress range vs. number of cycles. circumstances in reality [21]. Also, when MCS was implemented to institute a probabilistic model for estimating the propagation lifespan of the weld toe of aluminothermic rail welds, a semi-probabilistic approach should be considered including the influence of loads at the welds [40]. Subsequently, a non-deterministic probabilistic structural integrity model was created to account for the lifespan of weld toe cracks of the rail joints [48]. In comparison to current parametric equations, [PERSON] et al. 's proposed equation provides a more accurate forecast of the SIF in cracked tubular T/Y joints. According to the proposed equation's error analysis, 90% of the results have errors of less than 15%, and 99.7% have errors of less than 30% [30]. The fatigue fracture of austenitic stainless steel LCFW cruciform connections was discovered to be a semi-elliptic crack, one that was distinct from the fracture of LCFW structural steel, which implies higher fatigue and lower SIF [28]. However, it was demonstrated that in accordance with the guidelines of the International Institute of Welding (IIW) and Eurocode 3 (EC3), the fatigue strength of the LCFW cruciform joints made of austenitic stainless steel is higher than that of structural steel [35; 36]. The Bootstrap technique tends to be more consistent as it increases the degree of fitting in tests for fatigue in titanium alloy as well as the precision prediction of FL when the sample size is limited. Moreover, it was possible to obtain and verify a rather precise regression for the principal S-N curves of titanium alloy welded frames [39].