Nonlinear structural finite element model updating and uncertainty quantification

Hamed Ebrahimian; Rodrigo Astroza; Joel P. Conte

doi:10.1117/12.2084541

Nonlinear structural finite element model updating and uncertainty quantification

Hamed Ebrahimian, Rodrigo Astroza, Joel P. Conte

Research output: Contribution to conference › Paper

3 Scopus citations

Abstract

This paper presents a framework for nonlinear finite element (FE) model updating, in which state-of-the-art nonlinear structural FE modeling and analysis techniques are combined with the maximum likelihood estimation method (MLE) to estimate time-invariant parameters governing the nonlinear hysteretic material constitutive models used in the FE model of the structure. The estimation uncertainties are evaluated based on the Cramer-Rao lower bound (CRLB) theorem. A proof-of-concept example, consisting of a cantilever steel column representing a bridge pier, is provided to verify the proposed nonlinear FE model updating framework.

Original language	American English
DOIs	https://doi.org/10.1117/12.2084541
State	Published - 1 Jan 2015
Event	Proceedings of SPIE - The International Society for Optical Engineering - Duration: 1 Jan 2019 → …

Conference

Conference	Proceedings of SPIE - The International Society for Optical Engineering
Period	1/01/19 → …

Keywords

Bayesian inference
Cramer-Rao lower bound
Gradientbased optimization
model updating
nonlinear finite element model
nonlinear system identification
parameter estimation
structural damage identification
structural health monitoring
uncertainty quantification

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10.1117/12.2084541

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title = "Nonlinear structural finite element model updating and uncertainty quantification",

abstract = "This paper presents a framework for nonlinear finite element (FE) model updating, in which state-of-the-art nonlinear structural FE modeling and analysis techniques are combined with the maximum likelihood estimation method (MLE) to estimate time-invariant parameters governing the nonlinear hysteretic material constitutive models used in the FE model of the structure. The estimation uncertainties are evaluated based on the Cramer-Rao lower bound (CRLB) theorem. A proof-of-concept example, consisting of a cantilever steel column representing a bridge pier, is provided to verify the proposed nonlinear FE model updating framework.",

keywords = "Bayesian inference, Cramer-Rao lower bound, Gradientbased optimization, model updating, nonlinear finite element model, nonlinear system identification, parameter estimation, structural damage identification, structural health monitoring, uncertainty quantification, Bayesian inference, Cramer-Rao lower bound, Gradientbased optimization, model updating, nonlinear finite element model, nonlinear system identification, parameter estimation, structural damage identification, structural health monitoring, uncertainty quantification",

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T1 - Nonlinear structural finite element model updating and uncertainty quantification

AU - Ebrahimian, Hamed

AU - Astroza, Rodrigo

AU - Conte, Joel P.

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N2 - This paper presents a framework for nonlinear finite element (FE) model updating, in which state-of-the-art nonlinear structural FE modeling and analysis techniques are combined with the maximum likelihood estimation method (MLE) to estimate time-invariant parameters governing the nonlinear hysteretic material constitutive models used in the FE model of the structure. The estimation uncertainties are evaluated based on the Cramer-Rao lower bound (CRLB) theorem. A proof-of-concept example, consisting of a cantilever steel column representing a bridge pier, is provided to verify the proposed nonlinear FE model updating framework.

AB - This paper presents a framework for nonlinear finite element (FE) model updating, in which state-of-the-art nonlinear structural FE modeling and analysis techniques are combined with the maximum likelihood estimation method (MLE) to estimate time-invariant parameters governing the nonlinear hysteretic material constitutive models used in the FE model of the structure. The estimation uncertainties are evaluated based on the Cramer-Rao lower bound (CRLB) theorem. A proof-of-concept example, consisting of a cantilever steel column representing a bridge pier, is provided to verify the proposed nonlinear FE model updating framework.

KW - Bayesian inference

KW - Cramer-Rao lower bound

KW - Gradientbased optimization

KW - model updating

KW - nonlinear finite element model

KW - nonlinear system identification

KW - parameter estimation

KW - structural damage identification

KW - structural health monitoring

KW - uncertainty quantification

KW - Bayesian inference

KW - Cramer-Rao lower bound

KW - Gradientbased optimization

KW - model updating

KW - nonlinear finite element model

KW - nonlinear system identification

KW - parameter estimation

KW - structural damage identification

KW - structural health monitoring

KW - uncertainty quantification

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ER -

Nonlinear structural finite element model updating and uncertainty quantification

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