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Applications of Neural Networks in Modeling and Design of Structural Systems

  • Conference paper
Neural Networks in the Analysis and Design of Structures

Part of the book series: CISM International Centre for Mechanical Sciences ((CISM,volume 404))

Abstract

There has been considerable recent activity in exploring biological motivated computational paradigms in problems of engineering analysis and design. Such computational models are placed in a broad category of soft-computing tools that span the gap between traditional procedural methods of computation on one side, and heuristics driven inference engines (non procedural methods) on the other. Of these, methods of neural computing and evolutionary search have been extensively explored in problems of structural analysis and design. The purpose of the present chapter is two-fold. It provides an overview of those neural network architectures that are pertinent to the problem of structural analysis and design, including the back-propagation network, the counterpropagation network, the ART network, and the Hopfield network. It then provides a summary of select applications of neurocomputing in the field of structural synthesis. This summary includes the applications of neural networks in function modeling, in establishing causality in design data, in function optimization, and in diagnostics of structural systems.

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Author information

Authors and Affiliations

  1. Rensselaer Polytechnic Institute, Troy, NY, USA

    P. Hajela

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  1. P. Hajela
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Editor information

Editors and Affiliations

  1. Cracow University of Technology, Poland

    Zenon Waszczyszyn

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© 1999 Springer-Verlag Wien

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Hajela, P. (1999). Applications of Neural Networks in Modeling and Design of Structural Systems. In: Waszczyszyn, Z. (eds) Neural Networks in the Analysis and Design of Structures. CISM International Centre for Mechanical Sciences, vol 404. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2484-0_3

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  • DOI: https://doi.org/10.1007/978-3-7091-2484-0_3

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83322-3

  • Online ISBN: 978-3-7091-2484-0

  • eBook Packages: Springer Book Archive

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