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Identification of an equivalent model for granular soils by FEM/NMM/p-EMP hybrid system

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Języki publikacji
EN
Abstrakty
EN
The application of FEM/NMM/p-EMP computational hybrid system in formulation of the Neural Material Model (NMM) for granular soils is presented. NMM is a Multi Layer Preceptron formulated ’on-line’. The cumulative algorithm of the autoprogressive method was implemented into the FEM program. The patterns for NMM training were generated in the rigid strip footing analysis. Pseudo-empirical equilibrium paths p-EMP for veri?cation of the NMM were computed by a FEM program for the elastic-plastic Drucker-Prager material model. The discussed inverse problem of NMM identi?cation is illustrated by two study cases of footing: 1) rigid strip footing on plane semispace, 2) inclined slope analysis. It was numerically proved that the NMM identi?ed in the ?rst study case can be successfully applied to the analysis of the latter one.
Rocznik
Strony
283--290
Opis fizyczny
Bibliogr. 10 poz., rys., tab., wykr.
Twórcy
autor
  • Institute for Computational Civil Engineering Cracow University of Technology Warszawska 24, 31-155 Cracow, Poland, E.Pabisek@L5.pk.edu.pl
Bibliografia
  • [1] J. Ghaboussi, D.A. Pecknold, M. Zhang, R.M. Haj-Ali. Autoprogressive training of neural network constitutive models. Int. J. Num. Meth. Eng., 42: 105–126, 1998.
  • [2] Y.M. Hashash, S. Jung, J. Ghaboussi. Numerical implementation of a neural network based material model in finite element analysis. Int. J. Num. Meth. Eng., 59: 989–1005, 2004.
  • [3] S. Haykin. Neural Networks - A Comprehensive Foundation. Macmillan College Publ. Co., New York, 1994.
  • [4] E. Pabisek. Hybrid systems integrating FEM and ANN for the analysis of selected problems of structural and materials mechanics. Monograph 369, Cracow University of Technology, Series Civil Engineering, Cracow, 2008. (in Polish).
  • [5] E. Pabisek. On algorithms for identification of a neural network based model of equivalent material in real structures. Archives of Civ. Eng., 54(2): 395–404, 2008.
  • [6] E. Pabisek. Self-learning FEM/ANN approach to identification of equivalent material models for plane stress problem. Comput. Assis. Mech. Eng. Sci., 15: 67–78, 2008.
  • [7] H.S. Shin, G.N. Pande. On self-learning finite element codes based on monitored response of structure. Computer and Geotechnics, 27: 161–178, 2000.
  • [8] H.S. Shin, G.N. Pande. Identification of elastic constants for orthotropic materials from a structural test. Computer and Geotechnics, 30: 571–577, 2003.
  • [9] Z. Waszczyszyn and M. Słoński. Selected problems of artificial neural networks development. In Ch.5 Z. Waszczyszyn [Ed.], Advances of Soft Computing in Engineering, CISM Courses and Lecture, vol. 512, pp. 237–316, Springer, 2010.
  • [10] O.C. Zienkiewicz, A. Chan, M. Pastor, B. Schrefler,T. Shiomi. Computational Geomechanics. Wiley, Chichester, 1999.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPB2-0070-0012
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