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Stochastic finite element method based on point estimate and Karhunen–Loéve expansion

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Abstract

The present study proposes a new stochastic finite element method. The Karhunen–Loéve expansion is utilized to discretize the stochastic field, while the point estimate method is applied for calculating the random response of the structure. Two illustrative examples, including finite element models with one-dimensional and two-dimensional stochastic fields, are investigated to demonstrate the accuracy and efficiency of the proposed method. Furthermore, two classical finite element analysis methods are used to validate the results. It is proved that the proposed method can model both the one-dimensional and the two-dimensional stochastic finite element problems accurately and efficiently.

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  • 12 January 2021

    Journal abbreviated title on top of the page has been corrected to “Arch Appl Mech”

References

  1. Pask, J.E., Sukumar, N.: Partition of unity finite element method for quantum mechanical materials calculations. Extreme Mech. Lett. 11, 8–17 (2017). https://doi.org/10.1016/j.eml.2016.11.003

    Article  Google Scholar 

  2. Zhang, T.: Deriving a lattice model for neo-Hookean solids from finite element methods. Extreme Mech. Lett. 26, 40–45 (2019). https://doi.org/10.1016/j.eml.2018.11.007

    Article  Google Scholar 

  3. Schroder, J., Wriggers, P., Balzani, D.: A new Mixed Finite Element based on Different Approximations of the Minors of Deformation Tensors. Comput. Methods Appl. Mech. Eng. 200, 3583–3600 (2011). https://doi.org/10.1016/j.cma.2011.08.009

    Article  MathSciNet  MATH  Google Scholar 

  4. Schröder, J., Viebahn, N., Wriggers, P., Auricchio, F., Steeger, K.: On the stability analysis of hyperelastic boundary value problems using three-and two-field mixed finite element formulations. Comput. Mech. 60, 479–492 (2017).

  5. Schröder, J., Balzani, D., Brands, D.: Approximation of random microstructures by periodic statistically similar representative volume elements based on lineal-path functions. Arch. Appl. Mech. 81, 975–997 (2011). https://doi.org/10.1007/s00419-010-0462-3

    Article  MATH  Google Scholar 

  6. Sudret, B., Armen, D.K.: Stochastif finite element methods and reliability- A state of the art report

  7. Jiang, L., Liu, X., Xiang, P., Zhou, W.: Train-bridge system dynamics analysis with uncertain parameters based on new point estimate method. Eng. Struct. 199, 109454 (2019). https://doi.org/10.1016/j.engstruct.2019.109454

    Article  Google Scholar 

  8. Batou, A., Soize, C.: Stochastic modeling and identification of an uncertain computational dynamical model with random fields properties and model uncertainties. Arch. Appl. Mech. 83, 831–848 (2013). https://doi.org/10.1007/s00419-012-0720-7

    Article  MATH  Google Scholar 

  9. Shen, L., Ostoja-Starzewski, M., Porcu, E.: Bernoulli-Euler beams with random field properties under random field loads: fractal and Hurst effects. Arch. Appl. Mech. 84, 1595–1626 (2014). https://doi.org/10.1007/s00419-014-0904-4

    Article  MATH  Google Scholar 

  10. Kleiber, M., Hien, T.D.: The stochastic finite element method: basic perturbation technique and computer implementation. Wiley, New York (1992)

    MATH  Google Scholar 

  11. Ghanem, R.G., Spanos, P.D.: Stochastic finite elements: A spectral approach. Dover Publications, New York (2003)

    MATH  Google Scholar 

  12. Vanmarcke, E.H., Shinozuka, M., Nakagiri, S., Schueller, G.I., Grigoriu, M.: Random fields and stochastic finite elements. Struct. Saf. 3, 143–166 (1986). https://doi.org/10.1016/0167-4730(86)90002-0

    Article  Google Scholar 

  13. Mohammadi, J.: Reliability Assessment Using Stochastic Finite Element Analysis. J. Struct. Eng.-Asce. 127, 976–977 (2001). https://doi.org/10.1061/(ASCE)0733-9445(2001)127:8(976.2)

    Article  Google Scholar 

  14. Liu, C., Wang, T.-L., Qin, Q.: Study on sensitivity of modal parameters for suspension bridges. Struct. Eng. Mech. 8, 453–464 (1999) https://doi.org/10.12989/sem.1999.8.5.453

  15. Zhang, Y., Chen, S., Liu, Q., Liu, T.: Stochastic perturbation finite elements. Comput. Struct. 59, 425–429 (1996). https://doi.org/10.1016/0045-7949(95)00267-7

    Article  MATH  Google Scholar 

  16. Liu, W.K., Mani, A., Belytschko, T.: Finite element methods in probabilistic mechanics. Probabilistic Eng. Mech. 2, 201–213 (1987). https://doi.org/10.1016/0266-8920(87)90010-5

    Article  Google Scholar 

  17. Liu, W.K., Belytschko, T., Mani, A.: Probabilistic finite elements for nonlinear structural dynamics. Comput. Methods Appl. Mech. Eng. 56, 61–81 (1986). https://doi.org/10.1016/0045-7825(86)90136-2

    Article  MATH  Google Scholar 

  18. Shinozuka, M., Deodatis, G.: Response Variability of Stochastic Finite Element Systems. J. Eng. Mech.-Asce. 114, 499–519 (1988). https://doi.org/10.1061/(ASCE)0733-9399(1988)114:3(499)

    Article  Google Scholar 

  19. Deodatis, G., Graham, L.: The weighted integral method and the variability response function as part of a SFEM formulation. In: Uncertainty modeling in finite element, fatigue and stability of systems. pp. 71–116. World Scientific (1997)

  20. Graham, L., Deodatis, G.: Response and eigenvalue analysis of stochastic finite element systems with multiple correlated material and geometric properties. Probabilistic Eng. Mech. 16, 11–29 (2001). https://doi.org/10.1016/S0266-8920(00)00003-5

    Article  Google Scholar 

  21. Lei, Z., Qiu, C.: Neumann dynamic stochastic finite element method of vibration for structures with stochastic parameters to random excitation. Comput. Struct. 77, 651–657 (2000). https://doi.org/10.1016/S0045-7949(00)00019-5

    Article  Google Scholar 

  22. Lei, Z., Qiu, C.: A stochastic variational formulation for nonlinear dynamic analysis of structure. Comput. Methods Appl. Mech. Eng. 190, 597–608 (2000). https://doi.org/10.1016/S0045-7825(99)00431-4

    Article  MATH  Google Scholar 

  23. Füssl, J., Kandler, G., Eberhardsteiner, J.: Application of stochastic finite element approaches to wood-based products. Arch. Appl. Mech. 86, 89–110 (2016). https://doi.org/10.1007/s00419-015-1112-6

    Article  Google Scholar 

  24. Jiang, S.-H., Li, D.-Q., Zhang, L.-M., Zhou, C.-B.: Slope reliability analysis considering spatially variable shear strength parameters using a non-intrusive stochastic finite element method. Eng. Geol. 168, 120–128 (2014). https://doi.org/10.1016/j.enggeo.2013.11.006

    Article  Google Scholar 

  25. Wu, S.Q., Law, S.S.: Dynamic analysis of bridge with non-Gaussian uncertainties under a moving vehicle. Probabilistic Eng. Mech. 26, 281–293 (2011). https://doi.org/10.1016/j.probengmech.2010.08.004

    Article  Google Scholar 

  26. Wu, S.Q., Law, S.S.: Dynamic analysis of bridge–vehicle system with uncertainties based on the finite element model. Probabilistic Eng. Mech. 25, 425–432 (2010). https://doi.org/10.1016/j.probengmech.2010.05.004

    Article  Google Scholar 

  27. Ghanem, R.: Stochastic finite elements with multiple random non-Gaussian properties. J. Eng. Mech.-Asce. 125, 26–40 (1999). https://doi.org/10.1061/(ASCE)0733-9399(1999)125:1(26)

    Article  Google Scholar 

  28. Sepahvand, K.: Stochastic finite element method for random harmonic analysis of composite plates with uncertain modal damping parameters. J. Sound Vib. 400, 1–12 (2017). https://doi.org/10.1016/j.jsv.2017.04.025

    Article  Google Scholar 

  29. Sepahvand, K.: Spectral stochastic finite element vibration analysis of fiber-reinforced composites with random fiber orientation. Compos. Struct. 145, 119–128 (2016). https://doi.org/10.1016/j.compstruct.2016.02.069

    Article  Google Scholar 

  30. Sasikumar, P., Suresh, R., Gupta, S.: Stochastic finite element analysis of layered composite beams with spatially varying non-Gaussian inhomogeneities. Acta Mech. 1503–1522 (2014). https://doi.org/10.1007/s00707-013-1009-9

  31. Der Kiureghian, A., Ke, J.: The stochastic finite element method in structural reliability. Probabilistic Eng. Mech. 3, 83–91 (1988). https://doi.org/10.1016/0266-8920(88)90019-7

    Article  Google Scholar 

  32. Vanmarcke, E., Grigoriu, M.: Stochastic Finite Element Analysis of Simple Beams. J. Eng. Mech. 109, 1203–1214 (1983). https://doi.org/10.1061/(ASCE)0733-9399(1983)109:5(1203)

    Article  Google Scholar 

  33. Chen, J., Li, J.: Optimal determination of frequencies in the spectral representation of stochastic processes. Comput. Mech. 51, 791–806 (2013). https://doi.org/10.1007/s00466-012-0764-0

    Article  MathSciNet  MATH  Google Scholar 

  34. Zhang, J., Ellingwood, B.: Orthogonal Series Expansions of Random Fields in Reliability Analysis. J. Eng. Mech. 120, 2660–2677 (1994). https://doi.org/10.1061/(ASCE)0733-9399(1994)120:12(2660)

    Article  Google Scholar 

  35. Li, C., Der Kiureghian, A.: Optimal discretization of random fields. J. Eng. Mech.-Asce. 119, 1136–1154 (1993). https://doi.org/10.1061/(ASCE)0733-9399(1993)119:6(1136)

    Article  Google Scholar 

  36. Sudret, B., Der Kiureghian, A.: Stochastic finite element methods and reliability: a state-of-the-art report. Department of Civil and Environmental Engineering, University of California, Oakland (2000)

    Google Scholar 

  37. Rosenblueth, E.: Point estimates for probability moments. Proc. Natl. Acad. Sci. 72, 3812–3814 (1975). https://doi.org/10.1073/pnas.72.10.3812

    Article  MathSciNet  MATH  Google Scholar 

  38. Seo, H.S., Kwak, B.M.: Efficient statistical tolerance analysis for general distributions using three-point information. Int. J. Prod. Res. 40, 931–944 (2002). https://doi.org/10.1080/00207540110095709

    Article  MATH  Google Scholar 

  39. Yan-Gang, Zhao, Tetsuro, Ono: New Point Estimates for Probability Moments. J. Eng. Mech. 126, 433–436 (2000). https://doi.org/10.1061/(ASCE)0733-9399(2000)126:4(433)

    Article  Google Scholar 

  40. Zhou, J., Nowak, A.S.: Integration formulas to evaluate functions of random variables. Struct. Saf. 5, 267–284 (1988). https://doi.org/10.1016/0167-4730(88)90028-8

    Article  Google Scholar 

  41. Fan, W., Wei, J., Ang, A.H.-S., Li, Z.: Adaptive estimation of statistical moments of the responses of random systems. Probabilistic Eng. Mech. 43, 50–67 (2016). https://doi.org/10.1016/j.probengmech.2015.10.005

    Article  Google Scholar 

  42. Liu, X., Xiang, P., Jiang, L., Lai, Z., Zhou, T., Chen, Y.: Stochastic Analysis of Train-bridge System Using the Karhunen–Loeve Expansion and the Point Estimate Method. Int. J. Struct. Stab. Dyn. (2019). https://doi.org/10.1142/S021945542050025X

    Article  Google Scholar 

  43. Liu, X., Jiang, L., Lai, Z., Xiang, P., Chen, Y.: Sensitivity and dynamic analysis of train-bridge coupled system with multiple random factors. Eng. Struct. 221, 111083 (2020). https://doi.org/10.1016/j.engstruct.2020.111083

    Article  Google Scholar 

  44. Xu, H., Rahman, S.: A generalized dimension-reduction method for multidimensional integration in stochastic mechanics. Int. J. Numer. Methods Eng. 61, 1992–2019 (2004). https://doi.org/10.1002/nme.1135

    Article  MATH  Google Scholar 

  45. Zhao, Y., Lu, Z.: Cubic normal distribution and its significance in structural reliability. Struct. Eng. Mech. 28, 263–280 (2008). https://doi.org/10.12989/sem.2008.28.3.263

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Acknowledgements

The work described in this paper is supported by grants from the National Natural Science Foundation of China (Grant Nos. U1934207, 51778630, and 11972379), the Fundamental Research Funds for the Central Universities of Central South University (Grant No. 2020zzts148), Jiangsu Key Laboratory of Environmental Impact and Structural Safety in Engineering, China University of Mining & Technology and Engineering Research Center for Seismic Disaster Prevention and Engineering Geological Disaster Detection of Jiangxi Province (SDGD202001).

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Liu, X., Jiang, L., Xiang, P. et al. Stochastic finite element method based on point estimate and Karhunen–Loéve expansion. Arch Appl Mech 91, 1257–1271 (2021). https://doi.org/10.1007/s00419-020-01819-8

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