Structural Engineering and Mechanics

  • 제75권4호
  • /
  • Pages.519-527
  • /
  • 2020
  • /
  • 1225-4568(pISSN)
  • /
  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Dynamic response uncertainty analysis of vehicle-track coupling system with fuzzy variables

  • Ye, Ling (Engineering Research Center of Railway Environment Vibration and Noise, Ministry of Education, East China Jiaotong University) ;
  • Chen, Hua-Peng (Engineering Research Center of Railway Environment Vibration and Noise, Ministry of Education, East China Jiaotong University) ;
  • Zhou, Hang (Engineering Research Center of Railway Environment Vibration and Noise, Ministry of Education, East China Jiaotong University) ;
  • Wang, Sheng-Nan (Xiyi Road Sub-district Office, Organization Department)
  • 투고 : 2020.05.11
  • 심사 : 2020.06.03
  • 발행 : 2020.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

Dynamic analysis of a vehicle-track coupling system is important to structural design, damage detection and condition assessment of the structural system. Deterministic analysis of the vehicle-track coupling system has been extensively studied in the past, however, the structural parameters of the coupling system have uncertainties in engineering practices. It is essential to treat the parameters of the vehicle-track coupling system with consideration of uncertainties. In this paper, a method for predicting the bounds of the vehicle-track coupling system responses with uncertain parameters is presented. The uncertain system parameters are modeled as fuzzy variables instead of conventional random variables with known probability distributions. Then, the dynamic response functions of the coupling system are transformed into a component function based on the high dimensional representation approximation. The Lagrange interpolation method is used to approximate the component function. Finally, the bounds of the system's dynamic responses can be predicted by using Monte Carlo method for the interpolation polynomials of the Lagrange interpolation function. A numerical example is introduced to illustrate the ability of the proposed method to predict the bounds of the system's dynamic responses, and the results are compared with the direct Monte Carlo method. The results show that the proposed method is effective and efficient to predict the bounds of the system's dynamic responses with fuzzy variables.

키워드

과제정보

The authors are very grateful for the financial supports received from the Basic Research Program of China (Grant No. 216YFC0802002), the National Natural Science Foundation of China (Grant No. 51978263) and the Natural Science Key Foundation of Jiangxi Province (Grant No. 20192ACBL20008).

참고문헌

  1. Adhikari, S., Chowdhury, R. and Friswell, M.I. (2011), "High dimensional model representation method for fuzzy structure dynamics", J. Sound Vib., 330(11), 1516-1529. http;//doi.org/10.1016/j.jsv.2010.10.010.
  2. Alis, O.F. and Rabitz, H. (2001), "Efficient implementation of high dimensional model representations", J. Math. Chem., 29(2), 127-142. http;//doi.org/10.1023/A:1010979129659.
  3. Balu, A.S. and Rao, B.N. (2012), "High dimensional model representation based formulations for fuzzy finite element analysis of structures", Finite Elem. Anal. Des., 50, 217-230. http;//doi.org/10.1016/j.finel.2011.09.012.
  4. Chen, H.P. (2018), Structural Health Monitoring of Large Civil Engineering Structures, John Wiley and Sons, Hoboken, NJ, USA.
  5. Chen, H.P., Zhang, C. and Huang, T.L. (2017), "Stochastic modelling fatigue crack evolution and optimum maintenance strategy for composite blades of wind turbines", Struct Eng. Mech., 63(6), 703-712. https://doi.org/10.12989/sem.2017.63.6.703.
  6. Chen, H.P. and Xiao, N. (2015), "Symptom-based reliability analyses and performance assessment of corroded reinforced concrete structures", Struct Eng. Mech., 53(6), 1183-1200. https://doi.org/10.12989/sem.2015.53.6.1183.
  7. Chowdhury, R., Rao, B.N. and Prasad, A.M. (2008), "High dimensional model representation for piece wise continuous function approximation", Commun. Num. Methods Eng., 24(12), 1587-1609. http;//doi.org/10.1002/cnm.1053.
  8. Deng, L. and Cai, C.S. (2010), "Development of dynamic impact factor for performance evaluation of existing multi-girder concrete bridges", Eng. Struct., 32(1), 21-31. http;//doi.org/10.1016/j.engstruct.2009.08.013.
  9. Degrauwe, D., Lombaert, G. and Roeck, G.D. (2010), "Improving interval analysis in finite element calculations by means of affine arithmetic", Comput. Struct., 88(3-4), 247-254. http;//doi.org/10.1016/j.compstruc.2009.11.003.
  10. De, M.M., Moens, D., Desmet, W. and Vandepitte, D. (2008), "A response surface based optimization algorithm for the calculation of fuzzy envelope frfs of models with uncertain properties", Comput. Struct., 86(10), 1080-1092. http;//doi.org/10.1016/j.compstruc.2007.07.006.
  11. Doan, L.T.T., Amer, Y., Lee, S.H., Phuc, P.N.K. and Dat, L.Q. (2019), "A comprehensive reverse supply chain model using an interactive fuzzy approach-A case study on the Vietnamese electronics industry", Appl. Math. Model, 76, 87-108. https://doi.org/10.1016/j.apm.2019.06.003.
  12. De, M.M., Moens, D., Desmet, W. and Vandepitte, D. (2009), "An efficient response surface based optimization method for non-deterministic harmonic and transient dynamic analysis", CMES-Comp. Model. Eng., 47(2), 119-166. http;//doi.org/10.3970/cmes.2009.047.119.
  13. Farkas, L., Moens, D., Vandepitte, D. and Desmet, W. (2010), "Fuzzy finite element analysis based on reanalysis technique", Struct. Saf., 32(6), 442-448. http;//doi.org/10.1016/j.strusafe.2010.04.004.
  14. Hinke, L., Dohnal, F., Mace, B.R., Waters, T.P. and Ferguson, N.S. (2009), "Component mode synthesis as a framework for uncertainty analysis", J. Sound Vib., 324(1-2), 161-178. http;//doi.org/10.1016/j.jsv.2009.01.056.
  15. Liu, N., Gao, W., Song, C., Zhang, N. and Oi, Y.L. (2013), "Interval dynamic response analysis of vehicle-bridge interaction system with uncertainty", J. Sound Vib., 332(13), 3218-3231. http;//doi.org/10.1016/j.jsv.2013.01.025.
  16. Li, G., Rosenthal, C. and Rabitz, H. (2001), "High dimensional model representations", J. Phys. Chem. A, 105(33), 7765-7777. http;//doi.org/10.1021/jp010450t.
  17. Li, X., Jiang, H., Guo, S. and Ching, W.K. (2020), "On product of positive L-R fuzzy numbers and its application to multi-period portfolio selection problems", Fuzzy Optim. Decis. Ma., 19, 53-79. http;//doi.org/10.1007/s10700-019-09308-6.
  18. Ma, Y., Wang, L., Zhang, J., Peng, T. and Liu, Y. (2014), "Hybird uncertainty quantification for probabilistic corrosion damage prediction for aging RC bridges", J. Mater. Civ. Eng., 27(4), 04014152. http;//doi.org/10.1016/(asce)mt.1943-5533.0001096.
  19. Manson, G. (2005), "Calculating frequency response functions for uncertain systems using complex affine analysis", J. Sound Vib., 288(3), 487-521. http;//doi.org/10.1016/j.jsv.2005.07.004.
  20. Moore, R.E. (1966), Interval Analysis, Prentice-Hall, Englewood Cliffs, NJ, USA.
  21. Obrien, E.J., Cantero, D., Enright, B. and Gonzalez, A. (2010), "Characteristic dynamic increment for extreme traffic loading events on short and medium span highway bridges", Eng. Struct., 32(12), 3827-3835. http;//doi.org/10.1016/j.engstruct.2010.08.018.
  22. Qiu, Z.P., Wang, X.J. and Friswell, M.I. (2005), "Eigenvalue bounds of structures with uncertain-but-bounded parameters", J. Sound Vib., 282(1-2), 297-312. http;//doi.org/10.1016/j.jsv.2004.02.051.
  23. Sobol, I.M. (2003), "Theorems and examples on high dimensional model representation", Reliability Eng. Syst. Safe., 79(2), 187-193. http;//doi.org/10.1016/s0951-8320(02)00229-6.
  24. Tannert, T. and Haukaas, T. (2013), "Probabilistic models for structural performance of rounded dovetail joints", J. Struct. Eng., 139(9), 1479-1488. http;//doi.org/10.1016/(ASCE)ST.1943-541X.0000744.
  25. Wang, X.J., Yang, C., Wang, L. and Qiu, Z.P. (2014), "Probabilistic damage identification of structures with uncertainty based on dynamic responses", Acta Mech. Solida Sin., 27(2), 172-180. http;//doi.org/CNKI:SUN:GTLB.O.2014-02-007. https://doi.org/10.1016/S0894-9166(14)60027-6
  26. Wang, C. and Matthies, H.G. (2019), "Novel model calibration method via non-probabilistic interval characterization and Bayesian theory", Reliab. Eng. Syst. Safe., 183, 84-92. http;//doi.org/10.1016/j.ress.2018.11.005.
  27. Wang, C. and Qiu, Z.P. (2014), "Fuzzy finite difference method for heat conduction analysis with uncertain parameters", Acta Mech. Sinica-Prc, 30, 383-390. http;//doi.org/10.1007/s10409-014-0036-7.
  28. Ye, L., Zhu, H.P. and Weng, S. (2019), "Parameter identification of vehicle-track coupling system based on sensitivity analysis" (in Chinese), J. Civ. Eng. Manag., 36(4), 154-160. http;//doi.org/10.13579/j.cnki.2095-0985.2019.04.024.
  29. Zou, Q., Deng, L. and Jiang, C. (2016), "Predicting the bounds of vehicle-induced bridge responses using the interval analysis method", J. Bridge Eng., 21(9), 04016046. http;//doi.org/10.1016/(ASCE)BE.1943-5592.0000911.