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Structural identification with systematic errors and unknown uncertainty dependencies

James Alexandre Goulet and Ian F.C. Smith

Article (2013)

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When system identification methodologies are used to interpret measurement data taken from structures, uncertainty dependencies are in many cases unknown due to model simplifications and omissions. This paper presents how error-domain model falsification reveals properties of a structure when uncertainty dependencies are unknown and how incorrect assumptions regarding model-class adequacy are detected. An illustrative example is used to compare results with those from a residual minimization technique and Bayesian inference. Error-domain model falsification correctly identifies parameter values in situations where there are systematic errors, and can detect the presence 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: 09 Jun 2023 18:20
Cite in APA 7: Goulet, J. A., & 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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