# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
###### Abstract
For component testing, non-destructive post-production testing is pertinent for assessing the mechanical characteristics of manufactured components in industry [7]. Likewise, in order to verify structures using the fracture mechanic procedure, which is paramount for fatigue crack growth investigations, fatigue crack propagation limit curves can be determined to show that a flaw will not evolve to a critical size within a designated time or has an extended Fatigue Life (FL) than an adjacent standard weld detail. Furthermore, these curves are established by the simple and two-stage crack growth relationship which is based on the Paris-Erdogan law, and they aid in engineering critical assessment (ECA) and structural integrity calculations. Moreover, assessing a flaw in a flawed structure can help determine its fitness for service and calculate its defect tolerance. As a result, estimating the FL in welded joints is crucial for ensuring the reliability, safety, and endurance of various engineering structures and components. Typically, the FL phase is in three periods: initiation, crack growth, and final failure which are accounted for by the stress concentration factor, stress intensity factor, and fracture toughness, respectively [6; 8; 9]. Generally, the methods used to predict the FL of components or structures are physics based, data driven, or a combination of both as shown in Figure 1.  
Until recently, numerous numerical simulation techniques (physics based) have been used to address issues with FL prediction, which utilizes finite element programs to determine stresses and distributions in structures as well as model welds with second-order solid elements and determines the hot spot stress at the weld  
Figure 1: Research paradigm of FL prediction [10]. details with the quadratic surface stress extrapolation technique recommended by IIW [11]. However, their effectiveness is constrained by their high computing costs, model reliability, lack of self-improvement, and failure to grasp the variation law. Moreover, the coupling of multiple elements, including materials, size correlation, manufacturing methods, and service conditions, adds uncertainty to the safety of structures in certain applications. This prompted industry and academics to explore techniques that independently continuously improve while cutting costs and time. To highlight the relationship between influencing factors and targets, identify characteristics of the current damage state, and forecast fatigue life, the machine learning approach uses data from experiments and simulations [10]. Any machine learning method can also be expository (akin to finding previously unforeseen patterns), prognostic (akin to interpolations based on cognition), or stipulatory (akin to improvement based on machine learning driven decision making) [12]. Though, the focus of this research is an appraisal of their expository capabilities in the context of fatigue life, since the machine learning process has recently produced desirable results in solving damage problems in structures, which is the motivation for this study. In exemplification, [PERSON] et al. [13; 14; 15] was able to predict fatigue failure locations with regards to stress concentration factors in small-scale butt-welded joint specimens through explainable machine learning and deep neural networks. Also, [PERSON] et al. [16] evaluated the fatigue reliability of welded steel bridge decks subjected to stochastic truck loads employing machine learning, whose computational efficiency was demonstrated to be superior to the Monte Carlo simulation. Considering these prospects, it is possible to improve the methodologies for forecasting FL in a larger range of applications. Generally, the machine learning process typically begins with data (analytical, experimental, or numerical), then is followed by data exploration and preparation (data cleaning), model application, and lastly, model evaluation for performance as depicted in Figure 2 [17].