Prediction on the fatigue life of butt-welded specimens using artificial neural network

Steel and Composite Structures

Volumn 9, Issue 6, 2009, Pages 557-568

  Kim, Kyoung Nam ^a   Lee, Seong Haeng ^b   Jung, Kyoung Sup ^a  

^a Chungbuk National University   (South Korea)

^b Pusan National University   (South Korea)

Author keywords

Artificial neural network; Fatigue life; Fatigue strength; Prediction

Indexed keywords

BUTT WELDING; FATIGUE OF MATERIALS; FATIGUE TESTING; FORECASTING; MANUFACTURE; NEURAL NETWORKS; STRESS CONCENTRATION;

FATIGUE STRENGTH; HEAT ENERGY; LEARNING PROCESS; MANUFACTURING TIME; PREDICTION OF FATIGUE LIVES; STRESS CONCENTRATION FACTORS; THICK PLATE;

DEEP NEURAL NETWORKS;

EID: 73149109451     PISSN: 12299367     EISSN: None     Source Type: Journal    
DOI: 10.12989/scs.2009.9.6.557     Document Type: Article

References (17)

1. AASHTO LRFD Bridge Design Specification (2002), U.S. Units 2002, Interim Revisions.
2. Al-Jabri, K.S. and Al-Alawi, S.M. (2007), "Predicting the behavior of semi-rigid joints in fire using an artificial neural networks", Int. J. Steel Struct., 7(3), 209-217.
3. Amanullah, M., Siddiqui, N.A., Umar, A. and Abbas, H. (2002), "Fatigue reliability analysis of welded joints of a TLP tether system", Steel Compos. Struct., 2(5), 331-354.
4. Ayala-Uraga, E. and Moan, T. (2007), "Fatigue reliability assessment of welded joints applying consistent fracture mechanics formulations", Int. J. fatigue, 29, 444-456. (Pubitemid 44855118)
5. Bezazi, A., Gareth Pierce, S., Worden, K. and Harkati, E.H. (2007), "Fatigue life prediction of sandwith composite materials under flexural tests using a Bayesian trained artificial neural network", Int. J. fatigue, 29, 738-747. (Pubitemid 46016977)
6. Do, Y.T., Kim, I.G., Kim, J.W. and Park, C.H. (2001), Artificial intelligence-Concept and Application, Scitech Media, Korea.
7. Fathi, A. and Aghakouchak, A.A. (2007), "Prediction of fatigue crack growth rate in welded tubular joints using neural network", Int. J. fatigue, 29, 261-275. (Pubitemid 44556782)
8. Kang, J.Y., Choi, B.I., Lee, H.J., Kim, J.S. and Kim, K.J. (2006), "Neural network application in fatigue damage analysis under multiaxial random loadings", Int. J. fatigue, 28, 132-140.
9. Kim, J.T., Park, J.H., Koo, K.Y. and Lee, J.J (2008), "Acceleration-based neural networks algorithm for damage detection in structures", Smart Struct. Syst., 4(5), 583-603.
10. Kim, K.N., Lee, S.H. and Jung, K.S. (2009), "Evaluation of factors affecting the fatigue behavior of buttwelded joints using SM520C-TMC Steel", Int. J. Steel Struct., 9(3), 185-193.
11. Lee, Y.L., Tjhung, T. and Jordan, A. (2007), "A life prediction model for welded joints under multiaxial variable amplitude loading histories", Int. J. fatigue, 29, 1162-1173. (Pubitemid 46442707)
12. Li, B., Reis, L. and Freitas, M.D. (2006), "Simulation of cyclic stress/strain evolutions for multiaxial fatigue life prediction", Int. J. fatigue, 28, 451-458. (Pubitemid 43106641)
13. Majidian, A. and Saidi, M.H. (2007), "Comparison of Fuzzy logic and Neural Network in life prediction of boiler tubes", Int. J. fatigue, 29, 489-498. (Pubitemid 44855114)
14. McCulloch, W.S. and Pitts, W. (1943), "A Logical Calculus of the Ideas Imminent in Nervous Activity", B. Math. Bio., 5, 115-133.
15. Nishida, M. (1971), Stress Concentration, Morikita Ink, Japan.
16. Rumelhart, D.E., Hinton, G.E. and Williams, R.J. (1986), Learning Internal Representation by Error Propagation, Parallel Distributed Processing, Vol.1, MIT Press.
17. Yi, J.H., Yun, C.B. and Feng, M.Q. (2003), "Model updating and joint damage assessment for steel frame structures using structural identification techniques", Int. J. Steel Struct., 3(2), 83-94.