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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
CTU Central Library
2004-01-01
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| Series: | Acta Polytechnica |
| Subjects: | |
| Online Access: | https://ojs.cvut.cz/ojs/index.php/ap/article/view/636 |
| _version_ | 1818474542073380864 |
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| author | D. Novák D. Lehký |
| author_facet | D. Novák D. Lehký |
| author_sort | D. Novák |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T04:38:35Z |
| format | Article |
| id | doaj.art-ecb39101a8e2461793b5d749a2ad2170 |
| institution | Directory Open Access Journal |
| issn | 1210-2709 1805-2363 |
| language | English |
| last_indexed | 2024-04-14T04:38:35Z |
| publishDate | 2004-01-01 |
| publisher | CTU Central Library |
| record_format | Article |
| series | Acta Polytechnica |
| spelling | doaj.art-ecb39101a8e2461793b5d749a2ad21702022-12-22T02:11:47ZengCTU Central LibraryActa Polytechnica1210-27091805-23632004-01-01445-6636Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection CurveD. NovákD. LehkýA 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.https://ojs.cvut.cz/ojs/index.php/ap/article/view/636Neural networknonlinear fracture mechanicsLatin Hypercube Samplingidentification |
| spellingShingle | D. Novák D. Lehký Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve Acta Polytechnica Neural network nonlinear fracture mechanics Latin Hypercube Sampling identification |
| title | Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve |
| title_full | Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve |
| title_fullStr | Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve |
| title_full_unstemmed | Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve |
| title_short | Neural Network Based Identification of Material Model Parameters to Capture Experimental Load-deflection Curve |
| title_sort | neural network based identification of material model parameters to capture experimental load deflection curve |
| topic | Neural network nonlinear fracture mechanics Latin Hypercube Sampling identification |
| url | https://ojs.cvut.cz/ojs/index.php/ap/article/view/636 |
| work_keys_str_mv | AT dnovak neuralnetworkbasedidentificationofmaterialmodelparameterstocaptureexperimentalloaddeflectioncurve AT dlehky neuralnetworkbasedidentificationofmaterialmodelparameterstocaptureexperimentalloaddeflectioncurve |