Abstract
A common problem of excavation machinery based on mechanical actions is the unknown interaction of the cutting tools with geological settings. This interaction determines for different soils a different wear and consequently different economical costs for the excavation. We apply a strategy for soil modelling which is based on discretization of the continuum with rigid disks and suitable contact models and concentrate at contact level the real mechanical behaviour of the soil. In order to carry out the proposed strategy a “macro” and a “micro” level are established. In this paper an application of Artificial Neural Network (ANN) for identification of the parameters of the contact constitutive law is shown. The ANN is first trained using the theoretical results obtained from the developed numerical model. Results of some numerical tests concerning the choice of the proper topology of ANN, the best training set and the sensitivity of the identified parameters are shown.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Emeriault F. Cambou B. Micromechanical modeling of anisotropic non-linear elasticity of granular medium. Int. J. Solids Structures, Vol 33, No. 18, pp. 2591–2607 (1996).
Cundall P.A., Strack D.L. A discrete numerical model for granular assemblies, Géotechnique, 29, 47–65, 1979.
Zavarise G., Wriggers P., Stein E., Schrefler B. “Real Contact Mechanism and Finite Element Formulation-a Coupled Thermomechanical Approach”. Int. Jour. For Numerical Meth. in Eng. Vol 35 pgg. 767–785 (1992).
Pincus A. Approximation theory of the MLP model in neural networks. Acta Numerica. (1999). pp. 143–195.
Huber N. Tsakmakis Ch. A neural network tool for identifying the material parameters of a finite deformation viscoplasticity model with static recovery. Comp. Meth. Appl. Mech. Engr. 191 (2001) pp. 353–384.
Lefik M., Gawin D. Application of neural networks for the identification of function describing heat and mass sources intensity during hardening of fresh concrete. ECCM-2001 Creacow Poland.
G. Zavarise, A. Nardin, B.A. Schrefler. Discrete methods for soil modeling. Nafems “Conference and Users’ Meeting 2002”. (3–4 Ottobre 2002 Bergamo (Italy).
Okubo S., Fukui K.: Complete Stress_Strain Curves for Various Rock Types in Uniaxial Tension, Int. J. Rock Mech. Min. Sci. & Geomech. Abstr. Vol 33. No 6. pp 549–556.(1996).
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Nardin, A., Schrefler, B., Lefik, M. (2003). Application of Artificial Neural Network for Identification of Parameters of a Constitutive Law for Soils. In: Chung, P.W.H., Hinde, C., Ali, M. (eds) Developments in Applied Artificial Intelligence. IEA/AIE 2003. Lecture Notes in Computer Science(), vol 2718. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45034-3_55
Download citation
DOI: https://doi.org/10.1007/3-540-45034-3_55
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40455-2
Online ISBN: 978-3-540-45034-4
eBook Packages: Springer Book Archive