Abstract
This paper combines data from laboratory, centrifuge testing, and numerical tools to highlight the predictive capabilities of the Bayesian method for uncertainty quantification and propagation. The Bayesian approach is employed to estimate uncertain parameters of a multi-yield constitutive model using data from cyclic-triaxial testing. Then, predictive capabilities of a finite element model in reproducing the dynamic response of a saturated sand deposit are investigated by drawing samples from the estimated posterior probability distributions of the constitutive model parameters. Variability of the predicted responses due to estimation uncertainty is evaluated. The response of centrifuge tests is used to assess the simulated responses.
Original language | English |
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Pages (from-to) | 217-229 |
Number of pages | 13 |
Journal | Soil Dynamics and Earthquake Engineering |
Volume | 123 |
DOIs | |
State | Published - Aug 2019 |
Bibliographical note
Funding Information:This study was partially funded by the Department of Civil Engineering of the Universidad de Chile, making possible the leave of professor V. Mercado to the Universidad de Chile. R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT), through FONDECYT-Iniciación research grant No. 11160009. F. Ochoa-Cornejo acknowledges the financial support of CONICYT, Project FONDECYT-Iniciación No. 11181252, and from Universidad de Chile, Project U-Inicia Code N° UI 24/2018.
Funding Information:
This study was partially funded by the Department of Civil Engineering of the Universidad de Chile , making possible the leave of professor V. Mercado to the Universidad de Chile. R. Astroza acknowledges the financial support from the Chilean National Commission for Scientific and Technological Research (CONICYT) , through FONDECYT -Iniciación research grant No. 11160009 . F. Ochoa-Cornejo acknowledges the financial support of CONICYT, Project FONDECYT-Iniciación No. 11181252 , and from Universidad de Chile , Project U-Inicia Code N° UI 24/2018 .
Publisher Copyright:
© 2019 Elsevier Ltd