Coupled systems mechanics

  • 제11권1호
  • /
  • Pages.55-78
  • /
  • 2022
  • /
  • 2234-2184(pISSN)
  • /
  • 2234-2192(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Meso-scale based parameter identification for 3D concrete plasticity model

  • Suljevic, Samir (Universite de Technologie de Compiegne, Laboratoire Roberval de Mecanique, Centre de Recherche Royallieu) ;
  • Ibrahimbegovic, Adnan (Universite de Technologie de Compiegne, Laboratoire Roberval de Mecanique, Centre de Recherche Royallieu) ;
  • Karavelic, Emir (Faculty of Civil Engineering, University of Sarajevo) ;
  • Dolarevic, Samir (Faculty of Civil Engineering, University of Sarajevo)
  • 투고 : 2021.12.09
  • 심사 : 2021.12.29
  • 발행 : 2022.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

The main aim of this paper is the identification of the model parameters for the constitutive model of concrete and concrete-like materials capable of representing full set of 3D failure mechanisms under various stress states. Identification procedure is performed taking into account multi-scale character of concrete as a structural material. In that sense, macro-scale model is used as a model on which the identification procedure is based, while multi-scale model which assume strong coupling between coarse and fine scale is used for numerical simulation of experimental results. Since concrete possess a few clearly distinguished phases in process of deformation until failure, macro-scale model contains practically all important ingredients to include both bulk dissipation and surface dissipation. On the other side, multi-scale model consisted of an assembly micro-scale elements perfectly fitted into macro-scale elements domain describes localized failure through the implementation of embedded strong discontinuity. This corresponds to surface dissipation in macro-scale model which is described by practically the same approach. Identification procedure is divided into three completely separate stages to utilize the fact that all material parameters of macro-scale model have clear physical interpretation. In this way, computational cost is significantly reduced as solving three simpler identification steps in a batch form is much more efficient than the dealing with the full-scale problem. Since complexity of identification procedure primarily depends on the choice of either experimental or numerical setup, several numerical examples capable of representing both homogeneous and heterogeneous stress state are performed to illustrate performance of the proposed methodology.

키워드

과제정보

This work was supported by funding from ANR (project SELF-TUM), MEAE (project CESPA) and IUF (project MS1479). Samir Suljevic also acknowledges the French Embassy in Bosnia and Herzegovina scholarship for doctoral studies in France. All this support is gratefully acknowledged.

참고문헌

  1. Brancherie, D. and Ibrahimbegovic, A. (2008), "Novel anisotropic continuum-discrete damage model capable of representing localized failure: Part I: Theoretic formulation and numerical implementation", Eng. Comput., 26(1/2), 100-127. https://doi.org/10.1108/02644400910924825.
  2. Brancherie, D., Villon, P. and Ibrahimbegovic, A. (2008), "On a consistent field transfer in non linear inelastic analysis and ultimate load computation", Comput. Mech., 42(2), 213-226. https://doi.org/10.1007/s00466-007-0199-1.
  3. Chen, B. and Liu, J. (2004), "Experimental study on AE charactersitics of three-point bending concrete beams", Cement Concrete Res., 34, 391-397. https://doi.org/10.1016/j.cemconres.2003.08.021.
  4. Claire, D., Hild, F. and Roux, S. (2004), "A finite element formulation to identify damage fields: The equilibrium gap method", Int. J. Numer. Meth. Eng., 61, 189-208. https://doi.org/10.1002/nme.1057.
  5. Furukawa, R. and Yagawa, G. (1997), "Inelastic constitutive parameter identification using an evolutionary algorithm with continuous individuals", Int. J. Numer. Meth. Eng., 40, 1071-1090. https://doi.org/10.1002/(SICI)1097-0207(19970330)40:6<1071::AID-NME99>3.0.CO;2-8.
  6. Goldberg, D. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Glen View, IL.
  7. Hadzalic, E., Ibrahimbegovic, A. and Dolarevic, S. (2019), "Theoretical formulation and seamless discrete approximation for localized failure of saturated poro-plastic structure interacting with reservoir", Comput. Struct., 214, 73-93. https://doi.org/10.1016/j.compstruc.2019.01.003.
  8. Hautefeuille, M., Colliat, J.B., Ibrahimbegovic, A., Matthies, H.G. and Villon, P. (2012), "Multiscale approach to modeling inelastic behavior with softening", Comput. Struct., 94-95, 83-95. https://doi.org/10.1016/j.compstruc.2011.11.007
  9. Holland, J.H. (1975), Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA.
  10. Hrstka, O. and Kucerova, A. (2004), "Improvements of real coded genetic algorithms based on differential operators preventing the premature convergence", Adv. Eng. Softw., 35(3/4), 237-246. https://doi.org/10.1016/S0965-9978(03)00113-3.
  11. Hrstka, O., Kucerova, A., Leps, M. and Zeman, J. (2003), "A competitive comparison of different types of evolutionary algorithms", Comput. Struct., 81(18/19), 1979-1990. https://doi.org/10.1016/S0045-7949(03)00217-7.
  12. Iacono, C., Sluys, L.J. and van Mier, J.G.M. (2006), "Estimation of model parameters in nonlocal damage theories by inverse analysis techniques", Comput. Meth. Appl. Mech. Eng., 195(52), 7211-7222. https://doi.org/10.1016/j.cma.2004.12.033.
  13. Ibrahimbegovic, A. (2009), Nonlinear Solid Mechanics: Theoretical Formulations and Finite Element Solution Methods, Springer, Dordrecht, Germany.
  14. Ibrahimbegovic, A., Knopf-Lenoir, C., Kucerova, A. and Villon, P. (2004), "Optimal design and optimal control of elastic structures undergoing finite rotations", Int. J. Numer. Meth. Eng., 61(14), 2428-2460. https://doi.org/10.1002/nme.1150
  15. Ibrahimbegovic, A., Matthies, H.G. and Karavelic, E. (2020), "Reduced model of macro-scale stochastic plasticity identification by Bayesian inference: Application to quasi-brittle failure of concrete", Comput. Meth. Appl. Mech. Eng., 372, 113428. https://doi.org/10.1016/j.cma.2020.113428.
  16. Ibrahimbegovic, A., Rukavina, I. and Suljevic, S. (2021), "Multiscale model with embedded discontinuity discrete approximation capable of representing full set of 3D failure modes for heterogeneous materials with no scale separation", Int. J. Multisc. Comput. Eng., 20, 1-32. https://doi.org/10.1615/IntJMultCompEng.2021038378.
  17. Imamovic, I., Ibrahimbegovic, A., Knopf-Lenoir, C. and Mesic, E. (2015), "Plasticity-damage model parameters identification for structural connections", Couple. Syst. Mech., 4(4), 337-364. http://doi.org/10.12989/csm.2015.4.4.337.
  18. Karakasis, M.K. and Giannakoglou, K.C. (2004), "On the use of surrogate evaluation models in multi-objective evolutionary algorithms", ECCOMAS, 2004, 4th European Congress on Computational Methods in Applied Sciences and Engineering, Jyvaskyla.
  19. Karavelic, E., Ibrahimbegovic, A. and Dolarevic S. (2019), "Multi-surface plasticity model for concrete with 3D hardening/softening failure modes for tension, compression and shear", Comput. Struct., 221, 74-90. https://doi.org/10.1016/j.compstruc.2019.05.009.
  20. Kozar, I., Toric Malic, N. and Rukavina, T. (2018), "Inverse model for pullout determination of steel fibers", Couple. Syst. Mech., 7(2), 197-209. http://doi.org/10.12989/csm.2018.7.2.197.
  21. Kucerova, A., Brancherie, D. and Ibrahimbegovic, A. (2006), "Material parameter identification for damage models with cracks", Proceedings of the 8th International Conference on Computational Structures Technology, Civil-Comp Press Ltd, Stirling.
  22. Kucerova, A., Brancherie, D., Ibrahimbegovic, A., Zeman, J. and Bittnar, Z. (2009), "Novel anisotropic continuum-discrete damage model capable of representing localized failure of massive structures- Part II: Identification from tests under heterogeneous stress field", Eng. Comput., 26, 128-144. https://doi.org/10.1108/02644400910924834
  23. Kucerova, A., Leps, M. and Skocek, J. (2005), "Large black-box functions optimization using radial basis function networks", Proceedings of 8th International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, Ed. Topping, B.H.V., Civil-Comp Press Ltd, Stirling.
  24. Kucerova, A., Leps, M. and Zeman, J. (2007), "Back analysis of microplane model parameters using soft computing methods", Comput. Assist. Mech. Eng. Sci., 14(2), 219-242.
  25. Lagarias, J.C., Reeds, J.A. and Wright, M.H. (1998), "Convergence properties of the Nelder-Mead simplex method in low dimensions", SIAM J. Optim., 9(1), 112-147. https://doi.org/10.1137/S1052623496303470.
  26. Leps, M. (2005), Single and Multi-Objective Optimization in Civil Engineering, Evolutionary Algorithms and Intelligent Tools in Engineering Optimization, WIT Press, Southampton.
  27. Mahnken, R. (2004), "Identification of material parameters for constitutive equations", Encyclopedia of Computational Mechanics, Part 2: Solids and Structures, Chapter 19.
  28. Mahnken, R. and Stein, E. (1996), "Parameter identification for viscoplastic models based on analytical derivatives of a least-squares functional and stability investigations", Int. J. Plast., 12(4), 451-479. https://doi.org/10.1016/S0749-6419(95)00016-X.
  29. Maier, G., Bocciarelli, M., Bolzon, G. and Fedele, R. (2006), "Inverse analyses in fracture mechanics", Int. J. Fract., 138, 47-73. https://doi.org/10.1007/s10704-006-7153-7.
  30. Michalewicz, Z. (1999), Genetic Algorithms+Data Structures=Evolution Programs, 3rd Edition, Springer, New York, NY.
  31. Nakayama, H., Inoue, K. and Yoshimon, Y. (2004), "Approximate optimization using computational intelligence and its application to reinforcement of cable-stayed bridges", ECCOMAS 2004, 4th European Congress on Computational Methods in Applied Sciences and Engineering, Jyvaskyla, Finland.
  32. Novak, D. and Lehky, D. (2006), "ANN inverse analysis based on stochastic small-sample training set simulation", Eng. Appl. Artif. Intel., 19(7), 731-740. https://doi.org/10.1016/j.engappai.2006.05.003.
  33. Pichler, B., Lackner, R. and Mang, H.A. (2003), "Back analysis of model parameters in geotechnical engineering by means of soft computing", Int. J. Numer. Meth. Eng., 57(14), 1943-1978. https://doi.org/10.1002/nme.740.
  34. Pyrz, M. and Zairi, F. (2007), "Identification of viscoplastic parameters of phenomenological constitutive equations for polymers by deterministic and evolutionary approach", Model. Simul. Mater. Sci. Eng., 15, 85-103. https://doi.org/10.1088/0965-0393/15/2/006
  35. Waszczyszyn, Z. and Ziemianski, L. (2006), "Neurocomputing in the analysis of selected inverse problems of mechanics of structures and materials", Comput. Assist. Mech. Eng. Sci., 13(1), 125-159.
  36. Wilson, E.L. and Ibrahimbegovic, A. (1991), "A modified method of incompatible modes", Commun. Appl. Numer. Meth., 7, 187-194. https://doi.org/10.1002/cnm.1630070303.
  37. Yagawa, G. and Okuda, H. (1996), "Neutral networks in computational mechanics", Arch. Comput. Meth. Eng., 3(4), 435-512. https://doi.org/10.1007/BF02818935.