Structural identification using a nonlinear constraint satisfaction processor with interval arithmetic and contractor programming

•A new methodology is formulated for finite element model updating.•Partially described IEP is structured as a nonlinear constraint satisfaction problem.•Measurement uncertainties are reflected in the feasible parameter space.•Feasible parameter space is completely and crisply enclosed by interval m...

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Vydané v:Computers & structures Ročník 188; s. 1 - 16
Hlavní autori: Kernicky, Timothy, Whelan, Matthew, Rauf, Usman, Al-Shaer, Ehab
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Elsevier Ltd 01.08.2017
Elsevier BV
Predmet:
Eigenvalues
Feasibility studies
Finite element analysis
Finite element method
Finite element model updating
Interval arithmetic
Inverse problems
Mass-spring systems
Mathematical models
Microprocessors
Model updating
Nonlinear equations
Numerical analysis
Parameter estimation
Partially described inverse eigenvalue problem
Physical properties
Probability distribution
Shape optimization
Structural identification
Studies
Uncertainty
Uniqueness
Vibration-based structural health monitoring
Partially described inverse eigenvalue problem
Structural identification
Vibration-based structural health monitoring
Interval arithmetic
Finite element model updating
ISSN:0045-7949, 1879-2243
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Shrnutí:•A new methodology is formulated for finite element model updating.•Partially described IEP is structured as a nonlinear constraint satisfaction problem.•Measurement uncertainties are reflected in the feasible parameter space.•Feasible parameter space is completely and crisply enclosed by interval methods.•Methodology is demonstrated on both well-posed and ill-posed problems. Structural identification through finite element model updating has gained increased importance as an applied experimental technique for performance-based structural assessment and health monitoring. However, practical challenges associated with computability, feasibility, and uniqueness present in the structured nonlinear inverse eigenvalue problem develop as a result of the necessary use of partially described and incompletely measured mode shapes. As an alternative to direct methods and optimization-based approaches, this paper proposes a new paradigm for model updating that is based on formulating the structured inverse eigenvalue problem as a Constraint Satisfaction Problem. Interval arithmetic and contractor programming are introduced as a means for generating feasible solutions to a structured inverse eigenvalue problem within a bounded parameter search space. This framework offers the ability to solve under-determined and non-unique inverse problems as well as accommodate measurement uncertainty through relaxation of constraint equations. These abilities address key challenges in quantifying uncertainty in parameter estimates obtained through structural identification and enable the exploration of alternative solutions to the global minimum that may better reflect the true physical properties of the structure. These capabilities are first demonstrated using synthetic data from a numerical mass-spring model and then extended to experimental data from a laboratory shear building model. Lastly, the methodology is contrasted with probabilistic model updating to highlight the advantages and unique capabilities offered by the methodology in addressing the effects of measurement uncertainty on the parameter estimation.
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ISSN:0045-7949
1879-2243
DOI:10.1016/j.compstruc.2017.04.001