2008
DOI: 10.1016/j.cma.2007.07.034
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Model validation for structural dynamic analysis: An approach to the Sandia Structural Dynamics Challenge

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Cited by 30 publications
(16 citation statements)
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“…A valid model may not be unique but should be suitable enough to perform the task for which it was created for or should be "fit-for-purpose." (Zang et al 2008) In the Journal of Management in Engineering. Submitted May 4, 2013; accepted January 7, 2014; posted ahead of print January 9, 2014. doi:10.1061/(ASCE) ME.1943-5479.0000284 Copyright 2014 by the American Society of Civil Engineers M a n u s c r i p t…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…A valid model may not be unique but should be suitable enough to perform the task for which it was created for or should be "fit-for-purpose." (Zang et al 2008) In the Journal of Management in Engineering. Submitted May 4, 2013; accepted January 7, 2014; posted ahead of print January 9, 2014. doi:10.1061/(ASCE) ME.1943-5479.0000284 Copyright 2014 by the American Society of Civil Engineers M a n u s c r i p t…”
Section: Methodsmentioning
confidence: 99%
“…Model validation is the process of demonstrating or obtaining a condition with sufficiently accurate coefficients to provide an acceptable description of the behavior of the subject structure (Ewins 2000). Validation includes checking the prediction performance against reference data from other sources, which are typically obtained from a specially conducted test (Zang et al 2008). A valid model may not be unique but should be suitable enough to perform the task for which it was created for or should be "fit-for-purpose."…”
Section: Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…The accomplishment of the validation has been performed applying different methods, which all led to the conclusion of a validated model. Aside from a graphical validation applied by Zang [4], the validation procedure is based on quantitative, classical methods such as hypothesis testing [12,18], confidence intervals [15] and validation criteria as used in aerospace industry based on frequency deviations, test-orthogonality and crossorthogonality [14]. In addition, a validation metric based on a weighted measure of the differences between the experimental and analytical cumulative density functions has been introduced [29].…”
Section: Validation Of the Substructurementioning
confidence: 99%
“…More specifically, model updating in the context of the application example in dynamics, mainly involved the usage of KarhunenLoève (KL) expansion to represent the identified structural parameters in a stochastic manner in order to take into account the uncertainties (see [12,14,15,18,29]), whereas the random vectors in the KL-expansion are modelled using several methods: analytical probability density functions, such as the Gaussian [15,14] and log-normal distribution [15], Polynomial Chaos expansion [12], kernel density estimator [15,18] and empirical cumulative density functions [29] are used for the representation of the stochastic term in the KL-expansion. The suitability of the description of the unknown input parameters as uncorrelated Gaussian distributed random variables has also been explored [4]. The objective of the present paper is the extension of the approach for model updating presented in [24].…”
Section: Introductionmentioning
confidence: 99%