Structural identification with systematic errors and unknown uncertainty dependencies

•In model-based data-interpretation, uncertainty dependencies are in many cases unknown due to model simplifications and omissions.•Error-domain model falsification reveals properties of a structure when uncertainty dependencies are unknown.•Comparison with residual minimization technique and Bayesi...

Full description

Saved in:
Bibliographic Details
Published in:Computers & structures 2013-11, Vol.128, p.251-258
Main Authors: Goulet, James-A., Smith, Ian F.C.
Format: Article
Language:English
Subjects:
Adequacy
Bayesian
Bayesian analysis
Computer simulation
Dependencies
Falsification
Mathematical models
Optimization
Simplification
System identification
Systematic errors
Uncertainties
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•In model-based data-interpretation, uncertainty dependencies are in many cases unknown due to model simplifications and omissions.•Error-domain model falsification reveals properties of a structure when uncertainty dependencies are unknown.•Comparison with residual minimization technique and Bayesian inference. 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.
ISSN:0045-7949
1879-2243
DOI:10.1016/j.compstruc.2013.07.009