Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
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
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.
Ecsc Technical Research, Study on Design of Steel Building in Earthquake Zones, Eccs, Brussels, Belgium, 1986
Fausett, L., Fundamental of Neural Networks, Prentice Hall, New Jersey, 1994.
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.
Garrett, J.H., ET AL., “Engineering Application of Neural Networks”, Journal of Intelligent Manufacturing, 4, 1–21, 1993.
Hertz, J., Krogh, A. and Palmer, R., Introduction to the theory of neural computation, Addison-Wesley, 1991.
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.
Mcculloch, W.S. and Pins, W., “A Logical Calculus of Ideas Immanent in Nervous Activity”, Bulletin of Mathematical Biophysics, 5, 115–133, 1943.
Minsky, M. and Papert, S.A., Perceptrons, Mit Press, Cambridge, MA, 1969.
Ndumu, A.N., ET AL. “Simulating Physical Processes with Artificial Neural Networks”, International Conference on Engineering Applications of Neural Networks, 9–12, 1996.
Rosenblatt, F., “The Perceptron: a perceiving and recognizable automaton”, Report 85460–1, Project Para, Cornell Aeronautical Laboratory, Ithaca, New York, 1957.
Rumelhart, D.E., Mclelland, J.L and Williams, R.J., “Learning Internal Representations by Back-Propagating Errors”, Nature, 323, 533–536, 1986.
Takeuchi, J., Kosugi, Y., “Neural Network Representation of the Finite Element Method”, Neural Networks, 7 (2), 389–395, 1994.
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.
Widrow, B. and Hoff, M.E., “Adaptive switching circuits”, in 1960 Ire Wescon Convention Record, part 4, 96–104, New York, 1960.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Wien
About this paper
Cite this paper
Bento, J. (1998). Modelling Mechanical Behaviour without Mechanics. In: Tasso, C., de Arantes e Oliveira, E.R. (eds) Development of Knowledge-Based Systems for Engineering. International Centre for Mechanical Sciences, vol 333. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2784-1_4
Download citation
DOI: https://doi.org/10.1007/978-3-7091-2784-1_4
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82916-5
Online ISBN: 978-3-7091-2784-1
eBook Packages: Springer Book Archive