# 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  
These methods are a combination of two or more methods from the predefined groups and or a unique methodology. To illustrate this, an exemplification is depicted in Figure 11. Therewith, a unique hybrid Single-Parameter Decision-Theoretic Rough Set (SPDTRS-CS-ANN) technique, developed by [PERSON] et al. [96], was used to forecast fatigue properties of welded junctions. Figure 10: Fatigue reliability evaluation by SHM based method [89]. [PERSON] et al. [97] conducted a probe into the FL prognosis of fused tube connections within signal support systems. This was carried out through the Bayesian updating of the fatigue growth model, specifically, the stochastic coefficients. Conversely, the fracture approach and bi-linear fatigue technique were implemented by [PERSON] et al. [98] to analyze an offshore structure's safety through a marine riser's non-linear analysis. With FORM and the MCS approach, the failure probability was calculated. By applying Mixed Dimensional Modeling (MDM), MCS, Rainbow counting, and Palmgren-Miner's rule, [PERSON] et al. [99] forecasted a cable-stayed OSD's fatigue life. [PERSON] et al. [13], by deterministic simulation and probabilistic modeling, assessed the fatigue dependability of bridges under stochastic truck loads, whereas a maintenance decision model for steel bridges was provided by [PERSON] et al. [100] with a case study. Thus, with the Bayesian network alongside a probabilistic model, it was feasible to perform a root cause analysis and identify the factors that control the outcome of the model. Data-based models for the fatigue dependability of OSDs were developed by [PERSON] et al. [101] based on data from temperature, strain, and traffic monitoring. The model used a mix of the time-series model, polynomial regression models, and MCS. The outcomes measured the detrimental impact on FL caused by either greater heavy traffic loads or higher future pavement temperatures or both. [PERSON] et al. [102] assessed the fatigue strength of thin-walled laser welded joints made of mild and high-strength steel. FEA was used to determine the stress field in the notch ligament for each specimen shape in the database. Moreover, three typical stress approaches: the highest notch stress, the stress averaging approach, and the critical distance were used to quantify fatigue. Moreover, for laser lap welding, [PERSON] et al. [103] created a decoupled multi-physics simulation that takes the part-to-part gap into account. The methodology was created using the energy balancing method. The created model can be used to monitor important geometrical features while they are being created, and it is also a crucial tool for creating an adjustment that can be applied to remote laser welding. Based on the expected growth in traffic flow, with FEA, MCS, and regression, [PERSON] et al. [104] performed a fatigue evaluation of the steel floor of a bridge. The roof weld was among the structural features taken into consideration to be the most likely to crack according to the  
Figure 11: A R/H methodology utilized to forecast fatigue properties [96]. diaphrag section's fatigue damage assessment. [PERSON] et al. [105] considered determining the Macro-Crack Initiation Life (MCIL) of an OSD while looking at weld heterogeneity and diverse traffic loading. MCS and the adaptive multi-scale approach (XFEM and FEM) were used. Under coupled dynamic stresses, [PERSON] et al. [106] made a non-deterministic evaluation of fatigue in coastal thin bridges. Stochastic load models, multi-scale FEA, Rainflow counts, the linear fatigue damage rule, Support Vector Regression (SVR), and the derivation of a limit state function were used to achieve this. Using the 3D-extended finite element toolkit for Abaqus (XFA3D) and machine learning methods in a digital twin setting, [PERSON] et al.