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Two scales, hybrid model for soils, involving artificial neural network and finite element procedure

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A hybrid ANN-FE solution is presented as a result of two level analysis of soils: a level of a laboratory sample and a level of engineering geotechnical problem. Engineering properties of soils (sands) are represented directly in the form of ANN (this is in contrast with our former paper where ANN approximated constitutive relationships). Initially the ANN is trained with Duncan formula (Duncan and Chang [2]), then it is re-trained (calibrated) with some available experimental data, specific for the soil considered. The obtained approximation of the constitutive parameters is used directly in finite element method at the level of a single element at the scale of the laboratory sample to check the correct representation of the laboratory test. Then, the finite element that was successfully tested at the level of laboratory sample is used at the macro level to solve engineering problems involving the soil for which it was calibrated
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Bibliogr. 9 poz., rys.
  • Department of Geotechnics and Engineering Structures, Łódź Univeristy of Technology, al. Politechniki 6, 90-590 Łódź,
  • Department of Geotechnics and Engineering Structures, Łódź Univeristy of Technology, al. Politechniki 6, 90-590 Łódź,
  • [1] BOSO D.P., LEFIK M., SCHREFLER B.A., Generalised self consistent homogenisation as an inverse problem, ZAMM: Z. Angew. Math. Mech., 2010, 90, 847–860.
  • [2] DUNCAN J.M., CHANG C.Y., Nonlinear analysis of stress and strain in soils, Journal of the Soil Mechanics and Foundations Division, ASCE, 1970, 96(SM5), 1629–1653.
  • [3] DUNCAN J.M., BYRNE P., WONG K.S., MABRY P., Strength, stress–strain and bulk modulus parameters for finite element; analyses of stresses and movements in soil masses, Rep. No. UCB/GT/80-01, University of California, Berkeley, Calif., 1980.
  • [4] GHABOUSSI J. GARRETT J.H., WU X., Knowledge-Based Modelling of Material Behaviour with Neural Networks, Journal of Engineering Mechanics, 1991, 117, 132–151.
  • [5] LEFIK M., Artificial neural network as a numerical representation of an incremental constitutive law, Poromechanics II, J.-L. Auriault et al. (eds.), Balkema Publishers, 2002, 251– 257.
  • [6] LEFIK M., SCHREFLER B.A., Artificial neural network as an incremental non-linear constitutive model for a finite element code, Computer Methods in Applied Mechanics and Engineering, 2003, 192/28–30, 3265–3283.
  • [7] PINGYE GUO, WEI-CHAO LI, Development and implementation of Duncan-Chang constitutive model in GeoStudio2007, International Conference on Advances in Computational Modeling and Simulation, Procedia Engineering, 2012, 31, 395–402, available online at
  • [8] SHIN H.S., PANDE G.N., On self-learning finite element codes based on monitored response of structures, Computers and Geotechnics, 2000, 27, 161–178.
  • [9] STARK T.D., EBELING R.M., VETTEL J.J., Hyperbolic stress– strain parameters for silts, Journal of Geotechnical Engineering, 1994, 120, No. 2, 420–440.
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