Calibración automatizada de modelos numéricos en base a ensayos de columnas de hormigón
The calibration of complex numerical models is usually carried-out as a trial and error process whose success is influenced by the human factor. This work presents the applicability and efficiency of recent Bayesian computational algorithms for the calibration of a complex non-lineal mechanical mode...
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| Andere auteurs: | , , |
| Formaat: | article |
| Taal: | spa |
| Gepubliceerd in: |
2019
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| Onderwerpen: | |
| Online toegang: | http://repositorio.ulvr.edu.ec/handle/44000/3638 |
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Voeg label toe | Samenvatting: | The calibration of complex numerical models is usually carried-out as a trial and error process whose success is influenced by the human factor. This work presents the applicability and efficiency of recent Bayesian computational algorithms for the calibration of a complex non-lineal mechanical model based on experimental data. To this aim, the ABC-SubSim algorithm is described and applied for the calibration of the model with parameter uncertainty. Experimental test results from a reinforced concrete column subjected to lateral cyclic load, are used for the calibration process. The results show that the proposed tool reduces the uncertainty about the parameters and makes them learn from the data, thus giving the most suitable input parameters for the numerical estimate of the test results. |
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