Modelling Mechanical Behaviour without Mechanics

  • J. Bento
Conference paper
Part of the International Centre for Mechanical Sciences book series (CISM, volume 333)


The modelling of mechanical behaviour of structures or, simply, that of solids bodies, has undergone a process of enormous maturation through the history of Mechanics, in the last two centuries. Depending on the then existing scientific paradigms, each of the steps of improvement in the modelling of mechanical behaviour of solid bodies has taken various forms; however, regardless of using a more rational approach or one of, predominantly, an empirical nature, mechanics has been invoked as the obvious supporting discipline for the analysis and synthesis of behaviour. Hence, the immensely rich spectrum of modelling attitudes spanning from experimental, through purely theoretical to computational methods.


Artificial Neural Network Frictional Contact Trained Network Generalisation Capacity Prescribe Velocity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bento, J.P., Ndumu, D, D., “Application of Neural Networks to the Earthquake Resistant Analysis of Structures”, short contribution, EG-Sea-AI 3rd Workshop, Iain MacLeod (ed.), 111–112, Ross Priory, Scotland, 1996.Google Scholar
  2. Ecsc Technical Research, Study on Design of Steel Building in Earthquake Zones, Eccs, Brussels, Belgium, 1986Google Scholar
  3. Fausett, L., Fundamental of Neural Networks, Prentice Hall, New Jersey, 1994.Google Scholar
  4. Flood, I., Kartam, N., “Neural Networks in Civil Engineering, I: Principles’ and understanding; II: Systems and applications”, Journal of Computing in Civil Engineering, 2 (8), 131–162, 1994.CrossRefGoogle Scholar
  5. Garrett, J.H., ET AL., “Engineering Application of Neural Networks”, Journal of Intelligent Manufacturing, 4, 1–21, 1993.CrossRefGoogle Scholar
  6. Hertz, J., Krogh, A. and Palmer, R., Introduction to the theory of neural computation, Addison-Wesley, 1991.Google Scholar
  7. Martins, J.A.C. and Pinto DA Costa, A, A., “Stability of finite dimensional systems with unilaeral contact and friction: flat obstacle and linear elastic behaviour”, Report IC-Ist AI no.5/96, Instituto Superior Técnico, 1996.Google Scholar
  8. Mcculloch, W.S. and Pins, W., “A Logical Calculus of Ideas Immanent in Nervous Activity”, Bulletin of Mathematical Biophysics, 5, 115–133, 1943.CrossRefMATHGoogle Scholar
  9. Minsky, M. and Papert, S.A., Perceptrons, Mit Press, Cambridge, MA, 1969.MATHGoogle Scholar
  10. Ndumu, A.N., ET AL. “Simulating Physical Processes with Artificial Neural Networks”, International Conference on Engineering Applications of Neural Networks, 9–12, 1996.Google Scholar
  11. Rosenblatt, F., “The Perceptron: a perceiving and recognizable automaton”, Report 85460–1, Project Para, Cornell Aeronautical Laboratory, Ithaca, New York, 1957.Google Scholar
  12. Rumelhart, D.E., Mclelland, J.L and Williams, R.J., “Learning Internal Representations by Back-Propagating Errors”, Nature, 323, 533–536, 1986.CrossRefGoogle Scholar
  13. Takeuchi, J., Kosugi, Y., “Neural Network Representation of the Finite Element Method”, Neural Networks, 7 (2), 389–395, 1994.CrossRefGoogle Scholar
  14. Waszczyszyn, Z., “Standard versus refined neural networks applications in civil engineering problems: an overview”, Proceedings of the 2nd Conference on Neural Networks and Their Applications, 509–516, Czestochowa, Poland, 1996.Google Scholar
  15. Widrow, B. and Hoff, M.E., “Adaptive switching circuits”, in 1960 Ire Wescon Convention Record, part 4, 96–104, New York, 1960.Google Scholar

Copyright information

© Springer-Verlag Wien 1998

Authors and Affiliations

  • J. Bento
    • 1
  1. 1.Technical University of LisbonLisbonPortugal

Personalised recommendations