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Structural identification with systematic errors and unknown uncertainty dependencies

James-A. Goulet and Ian F.C. Smith

Article (2013)

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Cite this document: Goulet, J.-A. & Smith, I. F.C. (2013). Structural identification with systematic errors and unknown uncertainty dependencies. Computers & Structures, 128, p. 251-258. doi:10.1016/j.compstruc.2013.07.009
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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.

Open Access document in PolyPublie
Subjects: 1000 Génie civil > 1000 Génie civil
Department: Département des génies civil, géologique et des mines
Research Center: Non applicable
Date Deposited: 15 Jan 2018 15:16
Last Modified: 24 Oct 2018 16:12
PolyPublie URL: https://publications.polymtl.ca/2883/
Document issued by the official publisher
Journal Title: Computers & Structures (vol. 128)
Publisher: Elsevier
Official URL: https://doi.org/10.1016/j.compstruc.2013.07.009


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