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

[1]  Adrião Duarte Dória Neto,et al.  BUILDING OF CONSTANT LIFE DIAGRAMS OF FATIGUE USING ARTIFICIAL NEURAL NETWORKS , 2005 .

[2]  M. Bilgili,et al.  Application of artificial neural networks for the wind speed prediction of target station using reference stations data , 2007 .

[3]  O. Canyurt Fatigue strength estimation of adhesively bonded tubular joint using genetic algorithm approach , 2004 .

[4]  Shamim N. Pakzad,et al.  Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment , 2020, Comput. Aided Civ. Infrastructure Eng..

[5]  Wu Deng,et al.  Fatigue behaviors prediction method of welded joints based on soft computing methods , 2013 .

[6]  Xinjun Peng,et al.  TSVR: An efficient Twin Support Vector Machine for regression , 2010, Neural Networks.

[7]  Ahmet H. Ertas,et al.  Optimization of Fiber Reinforced Laminates for Maximum Fatigue Life using Particle Swarm Optimization , 2012 .

[8]  Can Berk Kalayci,et al.  A comprehensive review of deterministic models and applications for mean-variance portfolio optimization , 2019, Expert Syst. Appl..

[9]  Yousef Al-Assaf,et al.  Prediction of the fatigue life of unidirectional glass fiber/epoxy composite laminae using different neural network paradigms , 2002 .

[10]  C. Fei,et al.  Reliability-Based Fatigue Life Prediction for Complex Structure with Time-Varying Surrogate Modeling , 2018, Advances in Materials Science and Engineering.

[11]  P. K. Ray,et al.  Prediction of residual fatigue life under interspersed mixed‐mode (I and II) overloads by Artificial Neural Network , 2009 .

[12]  Hong Tae Kang,et al.  Prediction of fatigue life for spot welds using back-propagation neural networks , 2007 .

[13]  Surendra M. Gupta,et al.  A hybrid genetic algorithm for sequence-dependent disassembly line balancing problem , 2016, Ann. Oper. Res..

[14]  Il Seon Sohn,et al.  Fatigue Life Prediction of Spot-Welded Joint by Strain Energy Density Factor using Artificial Neural Network , 2000 .

[15]  Wu Deng,et al.  Fatigue life prediction for welding components based on hybrid intelligent technique , 2015 .

[16]  Weicheng Cui,et al.  A state-of-the-art review on fatigue life prediction methods for metal structures , 2002 .

[17]  Khaled Assaleh,et al.  Predicting Stock Prices Using Polynomial Classifiers: The Case of Dubai Financial Market , 2011, J. Intell. Learn. Syst. Appl..

[18]  Efstratios F. Georgopoulos,et al.  Comparison of genetic programming with conventional methods for fatigue life modeling of FRP composite materials , 2008 .

[19]  Ke Li,et al.  Life Prediction of Rolling Bearing Using Genetic Algorithm , 2011 .

[20]  Mariel Alfaro-Ponce,et al.  Fatigue damage effect approach by artificial neural network , 2019, International Journal of Fatigue.

[21]  Kyoung-jae Kim,et al.  Bankruptcy prediction modeling with hybrid case-based reasoning and genetic algorithms approach , 2009, Appl. Soft Comput..

[22]  O. W. Caldwell,et al.  THE CENTRAL ASSOCIATION OF SCIENCE AND MATHEMATICS TEACHERS , 1905 .

[23]  Paul Beiss,et al.  Application of neural networking for fatigue limit prediction of powder metallurgy steel parts , 2013 .

[24]  Yousef Al-Assaf,et al.  Fatigue life prediction of unidirectional glass fiber/epoxy composite laminae using neural networks , 2001 .

[25]  Jeffrey M. Keisler,et al.  Statistical models for estimating fatigue strain-life behavior of pressure boundary materials in light water reactor environments , 1996 .

[26]  M. Kamal,et al.  Fatigue Life Prediction Using Simplified Endurance Function Model , 2013 .

[27]  Shahaboddin Shamshirband,et al.  The use of SVM-FFA in estimating fatigue life of polyethylene terephthalate modified asphalt mixtures , 2016 .

[28]  Ingoo Han,et al.  Hybrid genetic algorithms and support vector machines for bankruptcy prediction , 2006, Expert Syst. Appl..

[29]  Kamran Javed,et al.  Probabilistic Fatigue Life Prediction of Dissimilar Material Weld Using Accelerated Life Method and Neural Network Approach , 2019, Comput..

[30]  Surendra M. Gupta,et al.  Simulated Annealing Algorithm for Solving Sequence-Dependent Disassembly Line Balancing Problem , 2013, MIM.

[31]  Baldev Raj,et al.  Low cycle fatigue and creep–fatigue interaction behavior of 316L(N) stainless steel and life prediction by artificial neural network approach , 2003 .

[32]  Dan Ma,et al.  Forecasting of the Fatigue Life of Metal Weld Joints Based on Combined Genetic Neural Network , 2010 .

[33]  Jan Adamowski,et al.  An ensemble wavelet bootstrap machine learning approach to water demand forecasting: a case study in the city of Calgary, Canada , 2017 .

[34]  Harald Zenner,et al.  Lifetime calculation under variable amplitude loading with the application of artificial neural networks , 2005 .

[35]  Vipin Wagare Fatigue Life Prediction of Spot Welded Joints: A Review , 2018 .

[36]  Sze-jung Wu,et al.  A Neural Network Integrated Decision Support System for Condition-Based Optimal Predictive Maintenance Policy , 2007, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[37]  H. S. Artem,et al.  On the estimation and optimization capabilities of the fatigue life prediction models in composite laminates , 2018, Journal of Reinforced Plastics and Composites.

[38]  Lanlan Zhang,et al.  Using Genetic Algorithm to Optimize Parameters of Support Vector Machine and Its Application in Material Fatigue Life Prediction , 2015 .

[39]  K Salmalian,et al.  Multi-objective evolutionary optimization of polynomial neural networks for fatigue life modelling and prediction of unidirectional carbon-fibre-reinforced plastics composites , 2010 .

[40]  Krishna Dutta,et al.  Low Cycle Fatigue Life Prediction of Al–Si–Mg Alloy Using Artificial Neural Network Approach , 2016, Transactions of the Indian Institute of Metals.

[41]  Miao Cai,et al.  Optimization of the fatigue life of Epoxy Molding Compounds based on BP neural network prediction model , 2008, 2008 International Conference on Electronic Packaging Technology & High Density Packaging.

[42]  Marina Franulović,et al.  Implementation of strain-life fatigue parameters estimation methods in a web-based system , 2011 .

[43]  Surendra M. Gupta,et al.  Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem , 2013, Expert Syst. Appl..

[44]  N. Li,et al.  A pattern recognition artificial neural network method for random fatigue loading life prediction , 2017 .

[45]  H. J. Rack,et al.  A neural network approach to elevated temperature creep–fatigue life prediction , 1999 .

[46]  Ibrahim M. Deiab,et al.  Using Artificial Neural Networks to Predict the Fatigue Life of Different Composite Materials Including the Stress Ratio Effect , 2011 .

[47]  A. Barać,et al.  Inhibitory effect of thyme and cinnamon essential oils on Aspergillus flavus: Optimization and activity prediction model development , 2015 .

[48]  C. Hsein Juang,et al.  Prediction of Fatigue Life of Rubberized Asphalt Concrete Mixtures Containing Reclaimed Asphalt Pavement Using Artificial Neural Networks , 2009 .

[49]  Youping Wu,et al.  Prediction of the fatigue life of natural rubber composites by artificial neural network approaches , 2014 .

[50]  N. A. Fellows,et al.  Artificial neural network for random fatigue loading analysis including the effect of mean stress , 2018, International Journal of Fatigue.

[51]  Shahrum Abdullah,et al.  Optimization of spring fatigue life prediction model for vehicle ride using hybrid multi-layer perceptron artificial neural networks , 2019, Mechanical Systems and Signal Processing.

[52]  Taskin Kavzoglu,et al.  A kernel functions analysis for support vector machines for land cover classification , 2009, Int. J. Appl. Earth Obs. Geoinformation.

[53]  Gin Boay Chai,et al.  Fatigue Life Prediction of GLARE Composites Using Regression Tree Ensemble‐Based Machine Learning Model , 2020, Advanced Theory and Simulations.

[54]  Zhi Qiang Gao,et al.  Application of Artificial Neural Network to Forecast the Tensile Fatigue Life of Carbon Material , 2008 .

[55]  El Kadi,et al.  Fatigue Life Prediction of Composite Materials: Artificial Neural Networks vs. Polynomial Classifiers , 2011 .

[56]  Luca Maria Gambardella,et al.  Ant Algorithms for Discrete Optimization , 1999, Artificial Life.

[57]  Omesh K. Chopra,et al.  Using artificial neural networks to predict the fatigue life of carbon and low-alloy steels , 2000 .

[58]  Can Berk Kalayci,et al.  An artificial bee colony algorithm with feasibility enforcement and infeasibility toleration procedures for cardinality constrained portfolio optimization , 2017, Expert Syst. Appl..

[59]  H. E. Kadi,et al.  Predicting the Fatigue Life of Different Composite Materials Using Artificial Neural Networks , 2010 .

[60]  Alireza Rahai,et al.  Relationship between fatigue life of asphalt concrete and polypropylene/polyester fibers using artificial neural network and genetic algorithm , 2015 .

[61]  Özler Karakaş,et al.  Bee colony intelligence in fatigue life estimation of simulated magnesium alloy welds , 2019, International Journal of Fatigue.

[62]  Seyed Mohammad Ali Razavi,et al.  Application of Image Analysis and Artificial Neural Network to Predict Mass Transfer Kinetics and Color Changes of Osmotically Dehydrated Kiwifruit , 2011 .

[63]  Ahmet H. Ertas,et al.  Design of fiber reinforced laminates for maximum fatigue life , 2010 .

[64]  Artificial Neural Network Model for Prediction of Fatigue Lives of Composites Materials , 2007 .

[65]  Keith Worden,et al.  Fatigue life prediction of sandwich composite materials under flexural tests using a Bayesian trained artificial neural network , 2007 .

[66]  Darryl P Almond,et al.  The use of neural networks for the prediction of fatigue lives of composite materials , 1999 .

[67]  A. Ghanbarzadeh,et al.  Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand est , 2010 .

[68]  Xiao Liu,et al.  Expert system for remnant life prediction of defected components under fatigue and creep-fatigue loadings , 2008, Expert Syst. Appl..

[69]  Yubin Lan,et al.  Review: Development of soft computing and applications in agricultural and biological engineering , 2010 .

[70]  Surendra M. Gupta,et al.  A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem , 2013, The International Journal of Advanced Manufacturing Technology.

[71]  Yu-Lin Han,et al.  Artificial neural network technology as a method to evaluate the fatigue life of weldments with welding defects , 1995 .

[72]  Efstratios F. Georgopoulos,et al.  Artificial neural networks in spectrum fatigue life prediction of composite materials , 2007 .

[73]  Surendra M. Gupta,et al.  A tabu search algorithm for balancing a sequence-dependent disassembly line , 2014 .

[74]  Ali Fatemi,et al.  Fatigue behavior and modeling of short fiber reinforced polymer composites: A literature review , 2015 .

[75]  Efstratios F. Georgopoulos,et al.  Modelling Fatigue Life of Multidirectional GFRP Laminates under Constant Amplitude Loading with Artificial Neural Networks , 2006 .

[76]  Jamal A. Abdalla,et al.  Modeling and simulation of low-cycle fatigue life of steel reinforcing bars using artificial neural network , 2011, J. Frankl. Inst..

[77]  Hossein Etemadi,et al.  A Genetic Programming Model for Bankruptcy Prediction: Empirical Evidence from Iran , 2009, Expert Syst. Appl..

[78]  A. Majidian,et al.  Comparison of Fuzzy logic and Neural Network in life prediction of boiler tubes , 2007 .

[79]  Hossein Bonakdari,et al.  Design criteria for sediment transport in sewers based on self-cleansing concept , 2014 .

[80]  F. Sonmez,et al.  Design optimization of fiber-reinforced laminates for maximum fatigue life , 2014 .

[81]  H. E. Kadi,et al.  Fatigue Life Prediction of Different Fiber-Reinforced Composites Using Polynomial Classifiers , 2011 .

[82]  Hai Bo Lin,et al.  Fatigue Life Prediction Based on GA-BP Algorithm , 2011 .

[83]  Alessio Tomasella,et al.  Fatigue life estimation of non‐penetrated butt weldments in ligth metals by artificial neural network approach , 2013 .

[84]  Can Berk Kalayci,et al.  An ant colony system empowered variable neighborhood search algorithm for the vehicle routing problem with simultaneous pickup and delivery , 2016, Expert Syst. Appl..

[85]  Olcay Ersel Canyurt,et al.  Fatigue strength estimation of butt welded joints in magnesium AZ31 alloy using the genetic algorithm , 2008 .

[86]  Guang-Chen Bai,et al.  Probabilistic LCF life assessment for turbine discs with DC strategy-based wavelet neural network regression , 2019, International Journal of Fatigue.

[87]  Mahmudur Rahman,et al.  Advances in fatigue life modeling: A review , 2018 .

[88]  Thomas E. McKee,et al.  Genetic programming and rough sets: A hybrid approach to bankruptcy classification , 2002, Eur. J. Oper. Res..

[89]  Huijin Jin,et al.  Prediction of Contact Fatigue Life of Alloy Cast Steel Rolls Using Back-Propagation Neural Network , 2013, Journal of Materials Engineering and Performance.

[90]  Young-Hoon Kim,et al.  An expert system for fatigue life prediction under variable loading , 2009, Expert Syst. Appl..

[91]  João Carlos Figueira Pujol,et al.  A neural network approach to fatigue life prediction , 2011 .

[92]  M. D. Mathew,et al.  A neural network model to predict low cycle fatigue life of nitrogen-alloyed 316L stainless steel , 2008 .

[93]  Farayi Musharavati,et al.  A Review on Fatigue Life Prediction Methods for Metals , 2016 .

[94]  I. Uygur,et al.  Fatigue Life Predictions of Metal Matrix Composites Using Artificial Neural Networks , 2014 .

[95]  Harald Zenner,et al.  Determination of S–N curves with the application of artificial neural networks , 1999 .

[96]  H Seçil Artem,et al.  Optimum design of fatigue-resistant composite laminates using hybrid algorithm , 2017 .

[97]  Kyriakos I. Kourousis,et al.  Sensitivity and optimisation of the Chaboche plasticity model parameters in strain-life fatigue predictions , 2017 .

[98]  Olcay Polat,et al.  A perturbation based variable neighborhood search heuristic for solving the Vehicle Routing Problem with Simultaneous Pickup and Delivery with Time Limit , 2015, Eur. J. Oper. Res..

[99]  D. N. Thatoi,et al.  Prediction of constant amplitude fatigue crack growth life of 2024 T3 Al alloy with R-ratio effect by GP , 2015, Appl. Soft Comput..

[100]  Zhiyong Chen,et al.  Fatigue life prediction for vibration isolation rubber based on parameter-optimized support vector machine model , 2018, Fatigue & Fracture of Engineering Materials & Structures.

[101]  Surendra M. Gupta,et al.  Ant colony optimization for sequence‐dependent disassembly line balancing problem , 2013 .

[102]  Yousef Al-Assaf,et al.  FATIGUE LIFE PREDICTION OF COMPOSITE MATERIALS USING POLYNOMIAL CLASSIFIERS AND RECURRENT NEURAL NETWORKS , 2007 .

[103]  Jamal A. Abdalla,et al.  Artificial Neural Network Predictions of Fatigue Life of Steel Bars Based on Hysteretic Energy , 2013, J. Comput. Civ. Eng..

[104]  F. Aymerich,et al.  Prediction of Fatigue Strength of Composite Laminates by Means of Neural Networks , 1997 .

[105]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[106]  Can Berk Kalayci,et al.  A survey of swarm intelligence for portfolio optimization: Algorithms and applications , 2018, Swarm Evol. Comput..

[107]  Surendra M. Gupta,et al.  A variable neighbourhood search algorithm for disassembly lines , 2015 .

[108]  Xinhua Yang,et al.  An Entropy-Based Neighborhood Rough Set and PSO-SVRM Model for Fatigue Life Prediction of Titanium Alloy Welded Joints , 2019, Entropy.

[109]  Ö. Karakas,et al.  Estimation of fatigue life for aluminium welded joints with the application of artificial neural networks , 2011 .

[110]  M. Abdel Wahab,et al.  Fatigue in Adhesively Bonded Joints: A Review , 2012 .

[111]  Surendra M. Gupta,et al.  Multi-objective fuzzy disassembly line balancing using a hybrid discrete artificial bee colony algorithm , 2015 .

[112]  J. Correia,et al.  Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network , 2020 .

[113]  Serkan Tapkın,et al.  Estimation of Fatigue Lives of Fly Ash Modified Dense Bituminous Mixtures Based on Artificial Neural Networks , 2014 .

[114]  Kenan Genel,et al.  Application of artificial neural network for predicting strain-life fatigue properties of steels on the basis of tensile tests , 2004 .