Soft computing methods for fatigue life estimation: A review of the current state and future trends

Kalaycı, Can Berk; Karagöz, Sevcan; Karakaş, Özler

doi:10.1111/ffe.13343

Cited by 53 publications

(51 citation statements)

References 137 publications

(476 reference statements)

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“…Fatigue is one of the most common material failure modes that harm the economy, technology, and safety 4 . The most frequent failure cases are due to fatigue damage 5 .…”

Section: Introductionmentioning

confidence: 99%

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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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“…Fatigue is one of the most common material failure modes that harm the economy, technology, and safety 4 . The most frequent failure cases are due to fatigue damage 5 .…”

Section: Introductionmentioning

confidence: 99%

“…The most frequent failure cases are due to fatigue damage 5 . For example, 80% of accidents in welding structures are caused by fatigue failures 4 over the past few years. Machine learning methods have been widely explored in the fatigue field 6 .…”

Section: Introductionmentioning

confidence: 99%

Fatigue modeling using neural networks: A comprehensive review

Chen

Liu

2022

Fatigue Fract Eng Mat Struct

117

View full text Add to dashboard Cite

show abstract

“…However, it is approved by developing Deep Neural Network (DNN) that has more hidden layers (more than 2), higher accuracy in the predicted results can be obtained with same or smaller data set [64,65]. The NNs are the most frequently employed machine learning approach for fatigue life prediction in the last decade [66]. A major reason for the popularity of the application of NNs on fatigue behavior analysis is that NNs are mostly utilized for universal function approximation.…”

Section: Introductionmentioning

confidence: 99%

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

Maleki

Ünal

Sahebari

et al. 2022

JMSE

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In this study, deep learning approach was utilized for fatigue behavior prediction, analysis, and optimization of the coated AISI 1045 mild carbon steel with galvanization, hardened chromium, and nickel materials with different thicknesses of 13 and 19 µm were used for coatings and afterward fatigue behavior of related specimens were achieved via rotating bending fatigue test. Experimental results revealed fatigue life improvement up to 60% after applying galvanization coat on untreated material. Obtained experimental data were used for developing a Deep Neural Network (DNN) modelling and accuracy of more than 99%.was achieved. Predicted results have a fine agreement with experiments. In addition, parametric analysis was carried out for optimization which indicated that coating thickness of 10–15 µm had the highest effects on fatigue life improvement.

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“…A damage mechanics‐based term for the fatigue damage degree was implemented into the pavement model. Besides, as an increasingly popular technology in the area of material characterization, 22,23 an artificial intelligence (AI) algorithm was applied in this study with NDT test and FE model updating for the backcalculation of damage density. Moreover, the development of this term with pavement service time in different materials and structures was characterized, and the comparison between this term and other indicators of fatigue damage was conducted.…”

Section: Introductionmentioning

confidence: 99%

Backcalculation of damage density of in‐service asphalt pavements using artificial intelligence‐based finite element model updating

Deng

Luo

Wang

2021

Fatigue Fract Eng Mat Struct

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Fatigue damage is a common distress significantly affecting asphalt pavements in the performance, economy, and safety aspects. Accordingly, this paper aims to propose a methodology to backcalculate a continuum damage mechanics (CDM)‐based damage density of in‐service asphalt pavements from field nondestructive testing (NDT). An artificial intelligence (AI)‐based finite element (FE) model updating was applied with the falling weight deflectometer (FWD) test results for the calibrations of the damage density and supporting layer moduli. The development of the damage density with pavement service time was categorized and fitted with proposed analytical models. The damage density was compared with field observations on the fatigue cracking performance of asphalt pavements. It was found that the damage density had close relations with the material type, structural configuration, and fatigue cracking area of the pavement. This study shows a promising application of the damage density in the evaluation and prediction of the fatigue damage of asphalt pavements, which will lead to a timely and cost‐effective pavement maintenance and rehabilitation.

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Soft computing methods for fatigue life estimation: A review of the current state and future trends

Cited by 53 publications

References 137 publications

Fatigue modeling using neural networks: A comprehensive review

Fatigue modeling using neural networks: A comprehensive review

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

Backcalculation of damage density of in‐service asphalt pavements using artificial intelligence‐based finite element model updating

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