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Neural networks for computing in the elastoplastic analysis of structures

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Abstract

A neural network model is proposed and studied for the treatment of elastoplastic analysis problems. These problems are formulated as Q.P.P.s with inequality subsidiary conditions. In order to treat these conditions the Hopfield model is appropriately generalized and a neural model is proposed covering the case of inequalities. Finally, the parameter identification problem is formulated as a supervised learning problem. Numerical applications close the presentation of the theory and the advantages of the neural network approach are illustrated.

Sommario

Si propone un modello di rete neurale con l'obiettivo di usarlo per la trattazione di problemi di analisi elastoplastica, formulati come problemi di programmazione quadratica con disequazioni sussidiarie. Allo scopo di trattare queste condizioni si generalizza il modello di Hopfield e si propone un modello neurale che copre il caso di disequazioni. Inoltre il problema di identificazione parametrica viene formulato come un problema di apprendimento guidato. La presentazione della teoria è seguita da esempi di applicazioni numeriche e dalla illustrazione dei vantaggi dell'uso delle reti neurali.

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Authors and Affiliations

  1. Dept. of Civil Engineering, Aristotle University, GR-54006, Thessaloniki, Greece

    A. V. Avdelas & P. D. Panagiotopoulos

  2. Dept. of Techn. Mechanics, RWTH, D-5100, Aachen, Germany

    P. D. Panagiotopoulos

  3. Dept. of Computer Sciences, Aristotle University, GR-54006, Thessaloniki, Greece

    S. Kortesis

Authors
  1. A. V. Avdelas
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  2. P. D. Panagiotopoulos
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  3. S. Kortesis
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Avdelas, A.V., Panagiotopoulos, P.D. & Kortesis, S. Neural networks for computing in the elastoplastic analysis of structures. Meccanica 30, 1–15 (1995). https://doi.org/10.1007/BF00987122

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  • DOI: https://doi.org/10.1007/BF00987122

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