Fatigue modeling using neural networks: A comprehensive review

Abstract

Neural network (NN) models have significantly impacted fatigue‐related engineering communities and are expected to increase rapidly due to the recent advancements in machine learning and artificial intelligence. A comprehensive review of fatigue modeling methods using NNs is lacking and will help to recognize past achievements and suggest future research directions. Thus, this paper presents a survey of 251 publications between 1990 and July 2021. The NN modeling in fatigue is classified into five applications: fatigue life prediction, fatigue crack, fatigue damage diagnosis, fatigue strength, and fatigue load. A wide range of NN architectures are employed in the literature and are summarized in this review. An overview of important considerations and current limitations for the application of NNs in fatigue is provided. Statistical analysis for the past and the current trend is provided with representative examples. Existing gaps and future research directions are also presented based on the reviewed articles.

Statistical Characterization of Stress Concentrations along Butt Joint Weld Seams Using Deep Neural Networks

“…Data-driven methods are increasingly used to predict fatigue of components and parts [28] but also to determine SCFs. In terms of computational efficiency, ANN, once trained, are clearly superior to other computer-based methods such as FE simulations [29]; however, ANN have very limited ability to make predictions outside the training data space [33][34][35].…”

“…Data-driven methods (i.e., machine learning) are increasingly used to predict fatigue of components and parts [28]. Machine learning using ANN is the most common approach [29] and is suitable when large, complex data sets are available and there is no accurate physical description of the phenomenon [30][31][32].…”

Fatigue Factor Assessment and Life Prediction of Concrete Based on Bayesian Regularized BP Neural Network

“…where α and β are the regularization parameters; E w is the penalty term of the loss function; and M is the number of connection weights. Weight is modified as indicated in Equation (6). When updating the gradient to realize weight decay, the weight is multiplied by a constant coefficient of less than 1.…”

“…Later, the stress ratio R [3], the failure probability P [4], and the loading frequency [5] were gradually integrated for practical applications. Despite their extensiveness and complexity, the proposed equations cannot be applied to all fatigue analyses and it is difficult to ensure accuracy, and a unanimous consensus on quantitative measurement of these factors has not been achieved [6]. On the other hand, the backpropagation neural network (BPNN) has emerged as the most widely used soft computing method, automatically approximating the training data.…”

Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

“…On the other hand, as reported in several recent studies [68,69], applications of DNN based modelling of fatigue behavior attracts lots of attention lately due to their higher performance and accuracy compared to the SNNs [70,71]. In addition, it should be mentioned that applications of NN-based models for fatigue behavior analyses are investigated in the comprehensive review study carried out by Chen and Liu [72].…”