On self-learning finite element codes based on monitored response of structures

Abstract In this paper, a strategy for developing self-learning finite element codes is presented. At the heart of these codes is a neural network based constitutive model (NNCM). In contrast to the normal practice of training neural networks for constitutive models with the data from homogenous material tests, training is accomplished here with stresses and strains at certain calibrating points in tests on structures where the stress/strain states are not homogenous. This strategy has a distinct advantage since a considerable effort is devoted by the experimentalists to achieve as homogenous state of stress/strain as possible. In many situations this is impractical for many reasons such as the samples being too small, precious or may require expensive methods of preparation.The methodology of self-learning finite element codes is illustrated by the solution of two heuristic boundary value problems. The first is a two-bar structure in which one of the bars is made of an ideally plastic or a strain softening material whilst the second bar is linear elastic. Computed load–deformation data of the structure are used for training of the neural network based constitutive model (NNCM) for the non-linear bar. It is shown that NNCM is capable of simulating the ideal plastic as well as the strain softening behaviour. The second problem simulates a plane stress panel of linear elastic material subjected to a concentrated vertical load at the top. The displacements at a number of monitoring points are used to train a NNCM. It is shown that the choice of the position of monitoring points affects the training programme and consequently the convergence of the NNCM predictions to standard solutions. The position of the load is then changed to demonstrate that the NNCM has been adequately trained to be able to perform analysis of any boundary value problem in which the material law corresponds to the trained NNCM. It is believed that the proposed technique of self-learning finite element codes will make a crucial impact on the methodology of engineering analyses and condition monitoring of structures.

[1]  G. N. Pande,et al.  Rheological model and finite element analysis of jointed rock masses reinforced by passive, fully-grouted bolts , 1994 .

[2]  Tadahiko Shiomi,et al.  Practical Programming in Computational Geomechanics: With Special Reference to Earthquake Engineering , 1999 .

[3]  S. Pietruszczak,et al.  Numerical analysis of concrete fracture based on a homogenization technique , 1997 .

[4]  Xiaobo Liu,et al.  Approach to constitutive relationships of a Ti-5AI-2Sn-2Zr-4Cr-4Mo alloy by artificial neural networks , 1998 .

[5]  E. N. Bromhead,et al.  Computer methods and advances in geomechanics: G. Beer, J.R. Booker and J.P. Carter (eds) Balkema, Rotterdam, 1991, £122, (3 volumes) ISBN (Set) 90-6191-189-3 , 1993 .

[6]  S. Pietruszczak,et al.  An elastoplastic constitutive model for concrete , 1988 .

[7]  G. Pande,et al.  Numerical modelling of brick masonry panels subject to lateral loadings , 1996 .

[8]  Dean L. Millar,et al.  On The Investigation Of A Multilayer Feedforward Neural Network Model Of Rock Deformability Behaviour , 1995 .

[9]  Sundaramoorthy Rajasekaran,et al.  CONSTITUTIVE MODELING OF CONCRETE USING A NEW FAILURE CRITERION , 1996 .

[10]  Mathew J. Palakal,et al.  Material model for composites using neural networks , 1993 .

[11]  D. Millar,et al.  Investigation of back-propagation artificial neural networks in modelling the stress-strain behaviour of sandstone rock , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[12]  John G. Michopoulos,et al.  Characterization of strain-induced damage in composites based on the dissipated energy density part I. Basic scheme and formulation , 1995 .

[13]  Y. M. Najjar,et al.  Utilizing computational neural networks for evaluating the permeability of compacted clay liners , 1996 .

[14]  H. H. Einstein.  5th International Congress on Rock Mechanics , 1983 .

[15]  Jamshid Ghaboussi,et al.  New nested adaptive neural networks (NANN) for constitutive modeling , 1998 .

[16]  Jamshid Ghaboussi,et al.  Autoprogressive training of neural network constitutive models , 1998 .

[17]  S Pietruszczak,et al.  CONSTITUTIVE RELATIONS FOR PARTIALLY SATURATED SOILS CONTAINING GAS INCLUSIONS. DISCUSSION AND CLOSURE , 1997 .

[18]  Yoh-Han Pao,et al.  Adaptive pattern recognition and neural networks , 1989 .

[19]  Qing Zhang,et al.  Application of neural network models to rock mechanics and rock engineering , 1991 .

[20]  R. W. Tock,et al.  Airbag fabric material modeling of nylon and polyester fabrics using a very simple neural network architecture , 1996 .

[21]  Rui Zhao,et al.  Stress-Strain Modeling of Sands Using Artificial Neural Networks , 1995 .

[22]  Genki Yagawa,et al.  Implicit constitutive modelling for viscoplasticity using neural networks , 1998 .