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International Journal for Multiscale Computational Engineering
Impact-faktor: 1.016 5-jähriger Impact-Faktor: 1.194 SJR: 0.554 SNIP: 0.82 CiteScore™: 2

ISSN Druckformat: 1543-1649
ISSN Online: 1940-4352

International Journal for Multiscale Computational Engineering

DOI: 10.1615/IntJMultCompEng.2013005821
pages 289-307


Michael J. Bogdanor
Civil and Environmental Engineering Department, Vanderbilt University, Nashville, Tennessee 37235, USA
Sankaran Mahadevan
Civil and Environmental Engineering Department, Vanderbilt University, Nashville, Tennessee 37235, USA
Caglar Oskay
Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, Tennessee 37235, USA


This manuscript investigates the use of Bayesian statistical methods for calibration and uncertainty quantification in rate-dependent damage modeling of composite materials. The epistemic and aleatory uncertainties inherent in the model prediction due to model parameter uncertainty, model form error, solution approximations, and measurement errors are investigated. Gaussian process surrogate models are developed to replace expensive finite element models in the analysis. A viscous damage model is employed with a solution algorithm designed for implementation within a commercial finite element software package (Abaqus). Experimental results from a suite of monotonic load tests conducted on unidirectional glass fiber reinforced epoxy composite samples at multiple strain rates and strain orientations are used to quantify the uncertainty in the prediction of the composite response within a Bayesian framework.


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