# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 4 Conclusions  
This study investigated the classes of ML algorithms for the FL forecast of welded articles from the standpoint of the material type, application, welding technique, input, and output parameters. From the trends, it could be observed that the neural network-based methods, numerical approach-based methods and the random/hybrid methods are the ones still mostly deployed to solve FL prediction problems, while various functional parametric equations have been developed over the years. Also, from the results, the latest algorithms have been able to improve the accuracy of the predictions in terms of MAPE, MAE, RMSE, and other evaluation metrics. Furthermore, with the continuous development of these techniques, the need to conduct complex and time-exhaustive experiments along with finite element simulations would be eliminated or reduced as predictions can now be accelerated. In addition, the most susceptible structural features and areas of maximum SCFs can now be deduced effectively. Moreover, it is evident that it is of utmost importance to be able to select the requisite machine learning algorithm and gather enough training data for a fatigue prediction case, while the capability to sort input and output parameters plays a vital role in the credibility of the algorithm. Meanwhile, the authentication and validation of the advanced algorithms cannot be complete without comparing the ML results with experimental and computational results taking into consideration in-service scenarios. Nonetheless, the random/hybrid taxonomy seems to the best candidate to predict FL in most cases as it combines the strengths of the individual algorithms. Illustratively, the data-driven LSTM would be a good fit for applications that require the long-term monitoring and updating of the parameters as shown in its accuracy for the fatigue damage prognosis of OSDs which would eventually benefit the inspection, administration, and decision making for the maintenance of bridges. Meanwhile, for a proper multi-scale fatigue investigation, inclusion distribution and uncertainties should be depicted in the prediction model. Finally, most of the applications have been on bridges, especially OSDs, with steel as the main material. There should be more research on how to apply machine learning algorithms to FL prediction in other sectors such as transport, energy, aerospace, and automotive sectors with focus on other materials and alloys including composites, magnesium, aluminum, and dissimilar combinations. Future research should also adopt a probabilistic or dual (probabilistic and deterministic) approach to FL prediction as well as the application of a digital twin framework to study the lifecycle of components and structures with respect to sources of uncertainties, axiality of fatigue loading, and fatigue  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Taxonomy** & **Material** & **Input** & **Output** & **Welding Process** & **Application** \\ \hline Random/hybrid & & Traffic-flow parameters (growth factors of traffic volume and vehicle weight) [16], crack length data and strain data [97], crack growth parameters [98], stress histories and cycles [99], fatigue crack growth parameters [100], pavement temperatures and heavy traffic intensities [101], heavy traffic intensities [102], heavy shape and melt pool [103], stress stress amplitudes [104], stress [105], vehicle loads (scale loads) [106], USF [107], correlated wind histories at critical state [108], USF [109], USF [110], USF [111], USF [112], stress time histories at critical state [113], USF  
evolution. In addition, incorporation of computer vision techniques could aid in efficient fatigue crack image analysis. Conceptualization, S.G.; methodology, S.G. and L.M. ; validation, S.G.; formal analysis, S.G.; investigation, S.G.; resources, L.M. and F.C. ; writing--original draft preparation, S.G.; writing, S.G.; supervision, L.M. and F.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding.