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Structural identification with systematic errors and unknown uncertainty dependencies

James Goulet, Ian F.C. Smith

Article (2013)

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When system identification methodologies are used to interpret measurement datataken from structures, uncertainty dependencies are in many cases unknown due tomodel simplifications and omissions. This paper presents how error-domain model falsificationreveals properties of a structure when uncertainty dependencies are unknownand how incorrect assumptions regarding model-class adequacy are detected. An illustrativeexample is used to compare results with those from a residual minimizationtechnique and Bayesian inference. Error-domain model falsification correctly identifiesparameter values in situations where there are systematic errors, and can detect thepresence of unrecognized systematic errors.
Subjects: 1000 Civil engineering > 1000 Civil engineering
Department: Department of Civil, Geological and Mining Engineering
PolyPublie URL: https://publications.polymtl.ca/2883/
Journal Title: Computers & Structures (vol. 128)
Publisher: Elsevier
DOI: 10.1016/j.compstruc.2013.07.009
Official URL: https://doi.org/10.1016/j.compstruc.2013.07.009
Date Deposited: 15 Jan 2018 15:16
Last Modified: 15 Nov 2022 17:43
Cite in APA 7: Goulet, J., & Smith, I. F.C. (2013). Structural identification with systematic errors and unknown uncertainty dependencies. Computers & Structures, 128, 251-258. https://doi.org/10.1016/j.compstruc.2013.07.009


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