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

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Whelan, M., Kernicky, T., Al-Shaer, E., & Rauf, U. (2017). Structural identification using a nonlinear constraint satisfaction processor with interval arithmetic and contractor programming. Computers And Structures. https://doi.org/doi:10.1016/j.compstruc.2017.04.001
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Description

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.

Details
Title

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

Author(s)

Whelan, Matthew
Kernicky, Timothy
Al-Shaer, Ehab
Rauf, Usman

UNC Charlotte Affiliate Type

Faculty

Extent

1 online resource

Date Created

2017

Material Type

Journal article

Genre

articles

Resource Type

text

Abstract

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.

Subjects--Topics

Structural health monitoring
Vibration--Data processing

Is Part Of

Computers and structures

Language

English

Published Version (Please cite this version)

doi:10.1016/j.compstruc.2017.04.001

Publisher's Version

https://doi.org/10.1016/j.compstruc.2017.04.001

Permalink

http://hdl.handle.net/20.500.13093/work:1506

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