Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve

Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve

A new approach is presented for identifying material model parameters. The approach is based on coupling stochastic nonlinear analysis and an artificial neural network. The model parameters play the role of random variables. The Monte Carlo type simulation method is used for training the neural netw...

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Bibliographic Details
Main Authors: D. Novák, D. Lehký
Format: Article
Language:English
Published: CTU Central Library 2004-01-01
Series:Acta Polytechnica
Subjects:
Neural network
nonlinear fracture mechanics
Latin Hypercube Sampling
identification
Online Access:https://ojs.cvut.cz/ojs/index.php/ap/article/view/636
Description
Summary:A new approach is presented for identifying material model parameters. The approach is based on coupling stochastic nonlinear analysis and an artificial neural network. The model parameters play the role of random variables. The Monte Carlo type simulation method is used for training the neural network. The feasibility of the presented approach is demonstrated using examples of high performance concrete for prestressed railway sleepers and an example of a shear wall failure. 
ISSN:1210-2709
1805-2363

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