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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 103
PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON SOFT COMPUTING TECHNOLOGY IN CIVIL, STRUCTURAL AND ENVIRONMENTAL ENGINEERING
Edited by: Y. Tsompanakis
Paper 7

Identification of Neural Network Based Material Models under Uncertainty

S. Freitag

Institute for Structural Mechanics, Ruhr University Bochum, Germany

Full Bibliographic Reference for this paper
S. Freitag, "Identification of Neural Network Based Material Models under Uncertainty", in Y. Tsompanakis, (Editor), "Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering", Civil-Comp Press, Stirlingshire, UK, Paper 7, 2013. doi:10.4203/ccp.103.7
Keywords: artificial neural networks, particle swarm optimization, uncertainty, constitutive.

Summary

In this paper, an approach for constitutive material modelling is presented, which is based on a combination of three soft computing strategies (uncertainty, artificial neural networks, and swarm intelligence). The uncertainty model fuzziness is applied to take imprecise measurements and imprecise boundary conditions of experimental investigations into account. Data series obtained from experimental tests are described as fuzzy processes, containing interval processes and deterministic processes as special cases. Feed forward and recurrent neural network approaches are presented, which can be used for three-dimensional constitutive material models in finite element analyses. For direction dependent material behaviour, special network structures are developed. Particle swarm optimization (PSO) is applied for identification of the network parameters. A PSO approach for deterministic parameters is extended to handle fuzzy network parameters. Direct and indirect network training strategies are presented using uncertain stress-strain or load-displacement data, respectively. For network training and validation, a concept for computational steering of experimental tests is introduced.

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