Structural Engineering and Mechanics

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

A fuzzy residual strength based fatigue life prediction method

  • Zhang, Yi (School of Civil & Environmental Engineering, Nanyang Technological University)
  • Received : 2015.01.23
  • Accepted : 2015.10.10
  • Published : 2015.10.25
⟨ Previous Next ⟩

초록

The fatigue damage problems are frequently encountered in the design of civil engineering structures. A realistic and accurate fatigue life prediction is quite essential to ensure the safety of engineering design. However, constructing a reliable fatigue life prediction model can be quite challenging. The use of traditional deterministic approach in predicting the fatigue life is sometimes too dangerous in the real practical designs as the method itself contains a wide range of uncertain factors. In this paper, a new fatigue life prediction method is going to be proposed where the residual strength is been utilized. Several cumulative damage models, capable of predicting the fatigue life of a structural element, are considered. Based on Miner's rule, a randomized approach is developed from a deterministic equation. The residual strength is used in a one to one transformation methodology which is used for the derivation of the fatigue life. To arrive at more robust results, fuzzy sets are introduced to model the parameter uncertainties. This leads to a convoluted fuzzy based fatigue life prediction model. The developed model is illustrated in an example analysis. The calculated results are compared with real experimental data. The applicability of this approach for a required reliability level is also discussed.

키워드

참고문헌

  1. Beer, M., Zhang, Y., Quek, S.T. and Phoon, K.K. (2013), "Reliability analysis with scarce information: Comparing alternative approaches in a geotechnical engineering context", Struct. Saf., 41, 1-10. https://doi.org/10.1016/j.strusafe.2012.10.003
  2. Cheng, G. and Plumtree, A. (1998), "A fatigue damage accumulation model based on continuum damage mechanics and ductility exhaustion", Int. J. Fatig., 20(7), 495-501. https://doi.org/10.1016/S0142-1123(98)00018-8
  3. Costa, J.D., Ferreira, J.A.M., Borrego, L.P. and Abreu, L.P. (2012), "Fatigue behaviour of AA6082 friction stir welds under variable loadings", Int. J. Fatig., 37, 8-16. https://doi.org/10.1016/j.ijfatigue.2011.10.001
  4. Cunha, D.J., Benjamin, A.C., Silva, R.C., Guerreiro, J.N.C. and Drach, P.R.C. (2014), "Fatigue analysis of corroded pipelines subjected to pressure and temperature loadings", Int. J. Press. Ves. Pip., 113, 15-24. https://doi.org/10.1016/j.ijpvp.2013.10.013
  5. Dai, J., Das, D., Ohadi, M. and Pecht, M. (2013), "Reliability risk mitigation of free air cooling through prognostics and health management", Appl. Energy, 111, 104-112. https://doi.org/10.1016/j.apenergy.2013.04.047
  6. Diamond, O. (1988), "Fuzzy least squares", Inform. Sci., 46(3), 141-157. https://doi.org/10.1016/0020-0255(88)90047-3
  7. Doudard, C., Calloch, S. and Cugy, P. (2005), "A probabilistic two-scale model for high-cycle fatigue life predictions", Fatig. Fract. Eng. Mater. Struct., 28(3), 279-288. https://doi.org/10.1111/j.1460-2695.2005.00854.x
  8. Fatemi, A. and Yang, L. (1998), "Cumulative fatigue damage and life prediction theories: a survey of the state of the art for homogeneous materials", Int. J. Fatig., 20(1), 9-34. https://doi.org/10.1016/S0142-1123(97)00081-9
  9. Friedman, M., Ming, M. and Kandel, A. (1998), "Fuzzy linear systems", Fuzzy Set. Syst., 96(2), 201-209. https://doi.org/10.1016/S0165-0114(96)00270-9
  10. Kim, J., Yi, J., Kim, J., Zi, G. and Kong, J.S. (2013), "Fatigue life prediction methodology using entropy index of stress interaction and crack severity index of effective stress", Int. J. Damage Mech., 22(3), 375-392. https://doi.org/10.1177/1056789512448803
  11. Kwofie, S. and Rahbar, N. (2013), "A fatigue driving stress approach to damage and life prediction under variable amplitude loading", Int. J. Damage Mech., 22(3), 393-404. https://doi.org/10.1177/1056789512449638
  12. Liu, Y. and Mahadevan, S. (2007), "Stochastic fatigue damage modeling under variable amplitude loading", Int. J. Fatig., 29(6), 1149-1161. https://doi.org/10.1016/j.ijfatigue.2006.09.009
  13. Lu, J. (1996),Handbook of Measurement Residual Stresses, Fiarmont Rress, United States.
  14. Luo, X., Luo, R. and Lytton, R.L. (2014), "Energy-based mechanistic approach for damage characterization of preflawed visco-elasto-plastic materials", Mech. Mater., 70, 18-32. https://doi.org/10.1016/j.mechmat.2013.11.008
  15. Manson, S.S. and Halford, G.R. (1981), "Practical implementation of the double linear damage rule and damage curve approach for treating cumulative fatigue damage", Int. J. Fract., 17(2), 169-192. https://doi.org/10.1007/BF00053519
  16. Meneghetti, G. (2007), "Analysis of the fatigue strength of a stainless steel based on the energy dissipation", Int. J. Fatig., 29(1), 81-94. https://doi.org/10.1016/j.ijfatigue.2006.02.043
  17. Miner, M.A. (1945), "Cumulative damage in fatigue", J. Appl. Mech., 68, 339-341.
  18. Moller, B. and Beer, M. (2004), Fuzzy Randomness-Uncertainty in Civil Engineering and Computational Mechanics, Springer, Berlin.
  19. Moller, B. and Beer, M. (2008), "Engineering computation under uncertainty-Capabilities of non-traditional models", Comput. Struct., 86(10), 1024-1041. https://doi.org/10.1016/j.compstruc.2007.05.041
  20. Peters, G. (1994), "Fuzzy linear-regression with fuzzy intervals", Fuzzy Set. Syst., 63(1), 45-55. https://doi.org/10.1016/0165-0114(94)90144-9
  21. Rathod, V., Yadav, O.P., Rathore, A. and Jain, R. (2011), "Probabilistic modeling of fatigue damage accumulation for reliability prediction", Int. J. Qual. Stat. Reliab., 2011, 1-10.
  22. Sankararaman, S. and Mahadevan, S. (2013), "Separating the contributions of variability and parameter uncertainty in probability distributions", Reliab. Eng. Syst. Saf, 112, 187-199. https://doi.org/10.1016/j.ress.2012.11.024
  23. Sankararaman, S., Ling, Y. and Mahadevan, S. (2011), "Uncertainty quantification and model validation of fatigue crack growth prediction", Eng. Fract. Mech, 78(7), 1487-1504. https://doi.org/10.1016/j.engfracmech.2011.02.017
  24. Skorupa, M. (1999), "Load interaction effects during fatigue crack growth under variable amplitude loading-a literature review. Part II: qualitative interpretation", Fatig. Fract. Eng. Mater. Struct., 22(10), 905-926. https://doi.org/10.1046/j.1460-2695.1999.00158.x
  25. Tanaka, H. (1987), "Fuzzy data analysis by possibilistic linear models", Fuzzy Set. Syst., 24, 363-375. https://doi.org/10.1016/0165-0114(87)90033-9
  26. Walley, P. (1991), Statistical Reasoning with Imprecise Probabilities, Chapman & Hall, London.
  27. Wu, W.F. and Huang, T.H. (1993), "Prediction of fatigue damage and fatigue life under random loading", Int. J. Press. Ves. Pip., 53(2), 273-298. https://doi.org/10.1016/0308-0161(93)90083-6
  28. Xiong, J.J. and Shenoi, R.A. (2011), Fatigue and Fracture Reliability Engineering, Springer Series in Reliability Engineering, Springer, Berlin.
  29. Zadeh, L.A. (1975), "The concept of a linguistic variable and its application to approximate reasoning", Inform. Sci., 8, 199-249. https://doi.org/10.1016/0020-0255(75)90036-5
  30. Zhang, J. and Wang, F. (2010), "Modeling of damage evolution and failure in fiber-reinforced ductile composites under thermomechanical fatigue loading", Int. J. Damage Mech., 19(7), 851-875. https://doi.org/10.1177/1056789509359650
  31. Zhang, Y. (2015a), "Comparing the robustness of offshore structures with marine deteriorations-a fuzzy approach", Adv. Struct. Eng., 18(8), 1159-1172. https://doi.org/10.1260/1369-4332.18.8.1159
  32. Zhang, Y. (2015b), "On the climatic uncertainty to the environment extremes: a Singapore case and statistical approach", Polish J. Environ. Stud., 24(3), 1413-1422. https://doi.org/10.15244/pjoes/31718
  33. Zhang, Y. and Cao, Y.Y. (2015), "A fuzzy quantification approach of uncertainties in an extreme wave height modeling", Acta Oceanologica Sinica, 34(3), 90-98. https://doi.org/10.1007/s13131-015-0636-5
  34. Zhang, Y., Beer, M. and Quek, S.T. (2015), "Long-term performance assessment and design of offshore structures", Comput. Struct., 154, 101-115. https://doi.org/10.1016/j.compstruc.2015.02.029
  35. Zhu, S.P, Huang, H.Z. and Wang, Z.L. (2011), "Fatigue life estimation considering damaging and strengthening of low amplitude loads under different load sequences using fuzzy sets approach", Int. J. Damage Mech, 20, 876-899. https://doi.org/10.1177/1056789510397077
  36. Zhu, S.P., Huang, H.Z., He, L.P., Liu, Y. and Wang, Z. (2012), "A generalized energy-based fatigue-creep damage parameter for life prediction of turbine disk alloys", Eng. Fract. Mech., 90, 89-100. https://doi.org/10.1016/j.engfracmech.2012.04.021
  37. Zhu, S.P, Huang, H.Z., Li, Y., Liu, Y. and Yang, Y. (2013a), "Probabilistic modeling of damage accumulation for time-dependent fatigue reliability analysis of railway axle steels", Proc. Inst. Mech. Eng. Part F: J. Rail Rapid Tran., 229(1), 23-33. https://doi.org/10.1177/0954409713496772
  38. Zhu, S.P., Huang, H.Z., Liu, Y., Yuan, R. and He, L.P. (2013b), "An efficient life prediction methodology for low cycle fatigue-creep based on ductility exhaustion theory", Int. J. Damage Mech., 22(4),556-571. https://doi.org/10.1177/1056789512456030
  39. Zuo, F.J., Wang, H.K., Zhu, S.P., Gao, H. and Huang, H.Z. (2014), "Stochastic fatigue life prediction based on residual strength", Proceedings of 2014 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2014), Dalian, China, July.

피인용 문헌

  1. Influence of Climate Change in Reliability Analysis of High Rise Building vol.2016, 2016, https://doi.org/10.1155/2016/5709245
  2. A probabilistic analysis of Miner's law for different loading conditions vol.60, pp.1, 2016, https://doi.org/10.12989/sem.2016.60.1.071
  3. Strength changes of 40 Cr steel subjected to cyclic torsion below the fatigue limit vol.60, pp.11, 2018, https://doi.org/10.3139/120.111249
  4. Fuzzy inference systems based prediction of engineering properties of two-stage concrete vol.19, pp.2, 2015, https://doi.org/10.12989/cac.2017.19.2.133
  5. A novel evidence theory model and combination rule for reliability estimation of structures vol.62, pp.4, 2015, https://doi.org/10.12989/sem.2017.62.4.507
  6. LMI based criterion for reinforced concrete frame structures vol.9, pp.4, 2020, https://doi.org/10.12989/acc.2020.9.4.407
  7. Advanced controller design for AUV based on adaptive dynamic programming vol.5, pp.3, 2015, https://doi.org/10.12989/acd.2020.5.3.233
  8. Modified algorithmic LMI design with applications in aerospace vehicles vol.8, pp.1, 2015, https://doi.org/10.12989/aas.2021.8.1.069
  9. Optimized AI controller for reinforced concrete frame structures under earthquake excitation vol.11, pp.1, 2021, https://doi.org/10.12989/acc.2021.11.1.001
  10. Smart structural control and analysis for earthquake excited building with evolutionary design vol.79, pp.2, 2015, https://doi.org/10.12989/sem.2021.79.2.131