# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 2 Materials and Methods
### Classification and Definitions
#### 2.4.6 Random/Hybrid Based Methods
[107] predicted a structure's FL and discovered that the adoption of a digital twin strategy for the complete life management of aging structures requires the incorporation of as-manufactured attributes and uncertainties. [PERSON] et al. [108], while examining the lifecycle of a welded A-type frame in a mining vehicle, considered the material attribute unpredictabilities along with load and structural geometry through experimental data, FEA, and NSGA-II. [PERSON] et al. [109] used the critical plane analysis, the augmented Kalman filters, the Craig-Bampton approach, and sparse measured strain locations to estimate the remaining useful life forecast of an amusement ride (roller coaster) subjected to multiaxial loads that are nonproportional. The effect of corrosion in dissimilar FSW high-strength aluminum alloys was examined by [PERSON] et al. [110]. Consequently, a fatigue model that considers microstructures was created. A collaboration of the entropy-based neighborhood rough set and PSO-SVRM model was created by [PERSON] et al. [111] for the motive of predicting titanium welded junctions' fatigue lives. In order to optimize fatigue reliability in welded A-type frames while weighing several sources of uncertainty, [PERSON] et al. [112] developed a solution, in which the following techniques were used: membership function, Latin hypercube sampling, response surface methodology, and genetic algorithm (GA). In another work by [PERSON] et al. [113], they created an intelligent approach for forecasting the life of aluminum welded connections after fatigue. MnS inclusion distributions in high-strength steel were investigated on a multi-scale extent by [PERSON] et al. [114]. This technique involved a modification of coefficient of variation of the mean near-neighbor distance of inclusions, serial sectioning technique, and principal component analysis. Furthermore, the computation of the fatigue crack initiation as well as the total FL were carried out after distributions of inclusions were applied to the model. The fatigue reliability of pipeline' weldments subject to negligible visible faults were evaluated by [PERSON] et al. [115]. According to the Bayesian technique, damage tolerance theory, and FORM, the pipeline weldments' total dependability is significantly influenced by the direction of the faults. In a review of artificial intelligence implementations for FSW by [PERSON] et al. [116], the author concluded that the ANN was the most popular ML technique. However, hybrid systems employed in conjunction with ANN have higher accuracy. [PERSON] et al. [117] investigated the impact of localized delta ferrite number and microstructural evolution on the rate of high-cycle fatigue crack initiation. Paris curves and MATLAB image processing programs demonstrated the influence. A probabilistic multi-scale framework was developed by [PERSON] et al. [118] to forecast steel bridges' fatigue lives utilizing a digital twin-driven architecture. Crystal plastic finite element method, the modified Fine and Bhat model, and Bayesian inference (Markov Chain Monte Carlo Sampling) were all used in the probabilistic multi-scale model. Through ANN, response surface modeling technique, hypothetical parametric 3D FEA, FORM, and using the SHM data of wave loads, wind loads, and soil attributes, [PERSON] et al. [119] conducted analogous research between the S-N and fracture mechanics (FM) method on the reliability prognosis of offshore wind turbine jacket foundations. [PERSON] et al. [120] used response surface methodology (RSM) and NSGA-II surrogate modeling to carry out the fatigue optimization of structural variables for OSDs. Meanwhile, a probabilistic fatigue crack growth evaluation of OSDs using mixed failure models, GPR,DBN, FEA, and the Probability Fatigue Crack Growth (PFCG) model was created by [PERSON] et al. [121]. [PERSON] et al. [122] used the LEFM and random forest approach to conduct a machine-learning-centered assessment of the impact of defect/inclusion on the fatigue behavior of steels. [PERSON] et al.