# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 3 Results and Discussion
Titanium welding can be made to satisfy the stringent requirements of the aerospace and contemporary automotive industries for welding defects because of the ability to forecast the quality of the fused zone provided by a neuro-fuzzy approach. Meanwhile, when predicting the various fatigue behaviors of titanium alloy welded joints, the PSO-BP approach performed better than the traditional BP technique [71; 72]. A developed hybrid technique makes it possible to anticipate the fatigue lives more correctly in titanium welded joints, which aids in the structure's dependability design [111]. The results from the use of a generalized fatigue reliability model based on ESS with other key parameters including arc voltage, arc current, welding speed, and interactions between efficiency and welding speed demonstrates that the suggested approach offers a workable solution to the problem of choosing an appropriate category for the assessment of the probabilistic FL of the welded details in steel bridges. Also, it was ascertained that the obtained failure probability and reliability index are both sensitive to growth of traffic volume [56]. The correctness of an approach to measure fatigue in OSDs is mostly dependent on the quantity of training samples, which unavoidably has an impact on computational efficiency. It was evident that using a learning machine and probabilistic modeling is far more effective than using a typical MCS [99]. Merging a model with a monitoring system while creating a maintenance decision model for a bridge has a considerable impact on the conclusions' level of uncertainty, even for aspects of the bridge that are not monitored. Meanwhile, the Bayesian technique produces statistical distributions for the formation of fatigue cracks, and equally compute failure probability [95; 100]. Contrarily, when evaluating the fatigue dependability of OSDs, the limit state equation is more complex while the variables have distinct probability distributions, whereas the numerical analytical resolutions are consequently challenging [47]. The findings of an OSD's fatigue reliability assessment show that as service life lengthens, the reliability indices decline dramatically. Likewise, the other three welded elements, apart from an RD detail, are unable to achieve the goal of fatigue reliability during the 100-year service life of the bridge. It is significant to observe that traffic data are necessary for the basic statistical aspects of OSD fatigue measurement. Moreso, a high traffic rate would result in a large sampling number, which would somewhat affect efficiency [99]. The negative impact on FL from rising future pavement temperatures, greater levels of heavy traffic, or both has been quantified by the findings of a data-based model for OSD fatigue reliability. In a case study of the application of the data-based model, it was demonstrated that increased temperature and high traffic levels together had a significant negative impact on fatigue dependability, with a commonly used limit being achieved up to 40 years sooner than under the 'no change' baseline scheme. Nevertheless, the LSTM method performs well, as evidenced by validation results from the fatigue damage prognosis of an OSD, where the mean percentage absolute errors of the two types of fatigue susceptible features were less than 10% [86]. Thanks to the method proposed by [PERSON] et al. [45], relatively extensive statistics on crane utilization may be produced in a short amount of time. The data showed that no influence of residual stresses on the FL of welded joints was detected in the case of bending loading compared to torsional bending that showed effects as depicted by experimental data and applied damage factors [65]. To forecast the residual stresses in RSW joints using ANN, the results show that the created ANN has a good accuracy in predicting maximum tensile residual stress based on the indicated spot-welding variables. In exemplification, it can dependably predict residual stress in the spot weld of 2 mm thick aluminum 6061-T6 sheets [73; 74].