# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 2 Materials and Methods
### Classification and Definitions
#### 2.4.4 Neural Network Based Methods  
Artificial neural networks simulate biological neuronal connections by representing them as weights between nodes. Each input is given a weight before being added together. A linear combination is the term used to describe this activity. Within networks, which can draw inferences from a complicated and seemingly unconnected set of information, self-learning resulting from experience is possible [68]. As a result, a set of neuronal connections that can be used to predict FL forms this group. A group of neuronal connections depicted in Figure 9. In this, Back-Propagation Neural Network (BPNN) and Non-dominated Sorting Genetic Algorithm II (NSGA-II) in conjunction with Ultrasonic Testing (UT) were used by [PERSON] et al. [69] to forecast the static strength and fatigue response of spot-welded connections. The Rough Set Theory-Ant Colony Optimization-Back Propagation Neural Network (RST-ACO-BPNN) methodology was used by [PERSON] et al. [70] to investigate a soft computing technique for predicting fatigue behaviors of welded junctions. To obtain forecast values (life cycles), several combinations of structural stress, ratios, thickness, and material type were used. Also, by using a Multilayer Feed Forward Neural Network (MFNN) and fuzzy C-means clustering, [PERSON] et al. [71] was able to predict and categorize the degrees of flaws in a TieAl4V alloy butt joint coalesced zone. Based on a hybrid intelligent technique, [PERSON] et al. [72] predicted welding components' fatigue lives. It applied the Particle Swarm Optimization (PSO) algorithm, BP neural network, and rough set theory. [PERSON] et al. [73] conducted a numerical analysis on the FL of spot-welded joints via FEA and ANN. Without performing any extra fatigue testing, the developed maximum stress equation can estimate the fatigue design criteria for spot-welded joints with optimal measurements. To forecast residual stresses in resistance spot-welded joints, [PERSON] et al. [74] also employed FEA along with ANN. The input  
Figure 9: Neural network-based method used to predict fatigue life [69]. parameters were the welding time, electrode force, and welding current while the output parameter was the remaining stress. The viability of super alloy for green and thermal power plants was examined by [PERSON] et al. [75]. In their investigation into the welding of dissimilar materials. Methods for accelerated life testing and neural networks were employed. Equally, [PERSON] et al. [76] applied the accelerated life technique and neural network approach to conduct a probabilistic FL prediction of dissimilar material fusion. Levenberg-Marquardt (LM) and Bayesian regularization (BR) were the two training techniques used to train the ANN. [PERSON] et al. [77] established the notch determinants for welded cruciform connection through numerical analysis and metamodeling. The developed techniques offer reliable ways to quickly and accurately derive stress concentration parameters that might also be incorporated into more sophisticated applications as programmed solutions. A computational and experimental survey on steel strands subjected to the coupling effect of cyclic loads and a salt spray environment was conducted by [PERSON] et al. [78]. In this study, the neural network method and multi-dimensional linear regression technique were used to set up a stochastic pitting-corrosion model. Therefore, the component's fatigue notch factor might then be calculated using the stress concentration factor. Sensitivity analysis of the ANNs in a set-up to predict C45 steel forging tools durability was carried out by [PERSON] et al. [79]. [PERSON] et al. [80] estimated the non-deterministic FL of welded plate connections under uncertainty in arctic areas by applying a Genetic Algorithm and Neural Network (GA-NN) approach coupled with a metamodel. [PERSON] et al. [81] implemented ANN for fatigue fracture identification in welded steel bridge structural components.