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5. Back analysis of microplane model parameters using soft computing methods

Author

Anna Kucerova, Lepš, M., and Zeman, J.

Subjects
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, I.2.6, Computer Science::Neural and Evolutionary Computation, Computer Science - Neural and Evolutionary Computing, Neural and Evolutionary Computing (cs.NE)
Abstract

A new procedure based on layered feed-forward neural networks for the microplane material model parameters identification is proposed in the present paper. Novelties are usage of the Latin Hypercube Sampling method for the generation of training sets, a systematic employment of stochastic sensitivity analysis and a genetic algorithm-based training of a neural network by an evolutionary algorithm. Advantages and disadvantages of this approach together with possible extensions are thoroughly discussed and analyzed., Comment: 21 pages, 27 figures, 7 tables

Published
2009
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