# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 3 Results and Discussion
It was discovered that the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization algorithm can be employed to improve model fitting to experimental data for RSW joints. Hence, the model based on BFGS enables accurate prediction of the F-N fatigue curves of RSW joints without the need for fatigue testing, thereby saving costs and time required for experimentation [132]. Meanwhile, the results of an analysis were used to train a Deep Neural Network (DNN) to be able to forecast the residual stress in the weld toe and center of the joint of butt-welded joints [87]. Moreover, welding residual stress can be precisely forecasted by using the transverse and bending stress components derived from the transverse constraint, bending constraint, and welding residual stress as predictors for learning the DNN model. Also, results from a study showed that the FCG behavior of the butt-welded specimen is more sensitive to the stress ratio than that of the base metal specimen [131]. Conversely, an accelerated lifespan model that was founded on calculations of the mean (\(\mu\)) and standard deviation (SD) of crack growth can be used for the fatigue design of welded structures [46]. Using neural networks and accelerated life test techniques, the FL and corrosion FL of welds between dissimilar materials were predicted. The projected results and the actual fatigue and corrosion fatigue lives were in good agreement. Particularly, the neural network prediction results outperformed the accelerated life technique in terms of accuracy. Over 90% of the predictions on average were accurate [75]. Similarly, the accelerated life testing method and artificial neural network approach (ANN) were used to evaluate the FL forecast of a dissimilar material weld. When training the ANNs, the Levenberg-Marquardt algorithm performed worse than the Bayesian regularization training approach. Concurrently, the accelerated life testing method was assessed for various distributions, and the most suitable distribution for the fatigue data was the Weibull distribution. Thus, data from FL tests were accelerated with 95% confidence using the Weibull distribution [76]. Meanwhile, it was observed that the FL decreased when the fracture initiation at the corrosion flaws was visible in dissimilar FSW welds. The fatigue life, however, was essentially independent of the exposure time [110]. In the estimation of SCFs for partial or fully penetrated welded cruciform connections with double-filled welds and K- or double-Y-butt welds, the best fatigue behavior was shown by the Concrete-Filled Integral Welded K-joint, which is followed by the Concrete-Filled Rectangular Hollow Section (CFRHS) K-joints, and Rectangular Hollow Section (RHS) K-joints, the latter of which has the worst fatigue behavior. Furthermore, BPNN models can be trustworthy substitutes for complex equations to forecast the distribution of SCFs at CFST Y-joint intersecting lines since their results are near to the FE results [84]. Utilizing data from small- and large-scale specimen strains, the technique devised for monitoring retrofitted web stiffness was proven effective. It has been discovered that damage assessments based on strains at the crucial hot point are well connected with the actual fracture depth [90]. It was discovered that the bi-linear techniques produce greater FL measures, which has an impact on the target dependability of the design along with inspection schemes in regard to offshore structures. In the case of such structures,these variations are significant, particularly when the stress range bounds have a greater proportion of low-amplitude cycles [98]. A welded A-type chassis's FL can be enhanced, and its mass can be reduced. By utilizing the fuzzy fatigue reliability optimization based on evolutionary algorithms, its reliability ranged from 69.47% to 95.12% [108]. Also, it was inferred that a better welding process can help improve the fatigue performance of welded A-type frame in a heavy off-road mining truck [112].