Prediction of fatigue–crack growth with neural network-based increment learning scheme

Ma, Xinran; He, Xiuli; Tu, Z. C.

doi:10.1016/j.engfracmech.2020.107402

Cited by 52 publications

(14 citation statements)

References 31 publications

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“…The physical meaning is that the slope of the N – a curve is larger than the real value, which means an unstable crack growth. Similar work can be found in Haynes et al 153 Ma et al 154 developed an FNN model for a – N curve extrapolation with small data size in another recent work. The data on the first part of the curve is used as training data to predict the remaining part, as shown in Figure 12.…”

Section: Review Of Nn Applications In Fatiguesupporting

confidence: 67%

See 1 more Smart Citation

Fatigue modeling using neural networks: A comprehensive review

Chen

Liu

2022

Fatigue Fract Eng Mat Struct

117

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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.

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Section: Review Of Nn Applications In Fatiguesupporting

confidence: 67%

“…Neural network (NN) trained by the first part of the a – N curve and making predictions for the residual part 154 [Colour figure can be viewed at wileyonlinelibrary.com]…”

Section: Review Of Nn Applications In Fatiguementioning

confidence: 99%

Fatigue modeling using neural networks: A comprehensive review

Chen

Liu

2022

Fatigue Fract Eng Mat Struct

117

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“…Accordingly, it is anticipated that surrogate modeling, advanced variance-reduction techniques, and ML methodologies as well as their combination will be further adopted in the field of probabilistic structural integrity and life prediction of nuclear components. [90][91][92][93][94] Moreover, further efforts are required to develop simple approaches for estimating extremely small probabilities when the number of MC samples needed for direct evaluation is prohibitive. One promising approach, called "damage fitting and extrapolation approach," was introduced in this paper (Section 2.3.3), which is expected to be readily adopted in probabilistic nuclear engineering applications.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

A review of probabilistic structural integrity assessment in the nuclear sector and possible future directions

Chavoshi

Booker

Bradford

et al. 2021

Fatigue Fract Eng Mat Struct

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There always exists uncertainty and variability in structural integrity assessments arising from lack of knowledge, modeling approximations, or differences between as-manufactured components and as-operated components. This may ultimately impact on reactor lives. Such uncertainties and variabilities can be understood and quantified using probabilistic techniques together with reliability-based acceptance criteria, facilitating the quantification and management of risk. This review article provides a systematic appraisal of the latest worldwide literature and also unpublished reports from EDF UK nuclear plants, giving an overview of the existing knowledge of probabilistic/reliability structural integrity methodologies and tools from a multifaceted stance, including failure modes, problem types, material types, employed codes, correlations, and probability distributions. Structural reliability analysis at different levels is discussed, and pertinent issues on convergence testing, required number of trails, sensitivity analysis, verification of individual analyses, extremely small probabilities, reactor-wide failure probabilities, and digital twin structural integrity monitoring are elaborated.

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“…Support Vector Machine (SVM) helps to deal with complex systems and corrupted data; this is performed using the structural risk minimization to get the regression hyperplane through nonlinear transformation satisfying the Mercer's condition. Gradient Boosting Regression (GBR) is a prediction approach that combines machine learning and statistical boosting [131] , [132] , [133] . A Recurrent Neural Network (RNN) has feedback connections to perform the current prediction using the input data and the previous outputs; this can generate large or small gradients [134] , [135] .…”

Section: Fatiguementioning

confidence: 99%

Harbor and coastal structures: A review of mechanical fatigue under random wave loading

Jimenez-Martinez

2021

Heliyon

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Harbor and coastal structures are essential in maritime connections. Additionally, some offshore structures near the coast are important for supplying energy as a material or transforming the natural resources into energy, as in wind turbines. One of the main issues that needs to be overcome in terms of these structures is mechanical fatigue due to the loads of the structure by its function and waves, wind, the current seawater level, and ice. Structural design has to meet high target loads to ensure that structures can endure extreme marine conditions under the assumption of the probability of loads and based on marine conditions, can mask damage where component immersions are not available for inspection. In this work, the wave loads and fatigue damage under random processes are reviewed.

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Prediction of fatigue–crack growth with neural network-based increment learning scheme

Cited by 52 publications

References 31 publications

Fatigue modeling using neural networks: A comprehensive review

Fatigue modeling using neural networks: A comprehensive review

A review of probabilistic structural integrity assessment in the nuclear sector and possible future directions

Harbor and coastal structures: A review of mechanical fatigue under random wave loading

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