Structural model updating using incomplete transfer function and modal data

Farshadi, Maria; Esfandiari, Akbar; Vahedi, Maryam

doi:10.1002/stc.1932

Cited by 21 publications

(18 citation statements)

References 46 publications

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“…These error contaminated parameters θ are generated as normally distributed variables θ ∈ Ν( θ 0 , σ ), where θ are the natural frequencies of the first five modes, ω i , coordinates of the mode shapes of the first five modes, ϕ ij , and temperature measurements, T i , shown in Figure ; mean value θ 0 is the corresponding error free value of the parameter; standard deviation σ = r · θ 0 with the coefficient of variation (COV) r ; three different COVs are applied, r = 0.1%, 0.2%, and 0.5%, representing different levels of error. Similar levels of COVs can be found in these works …”

Section: Numerical Studysupporting

confidence: 87%

Environmental-effects-embedded model updating method considering environmental impacts

Zhou

Song

2017

Struct Control Health Monit

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Summary In structural health monitoring, one practical challenge is to separate the change of structural characteristics (e.g., natural frequency and mode shape) due to environmental impacts from those induced by actual damage. Generally, data‐driven regression models are applied to remove the environmental impacts before model updating takes place. Model selection and training procedures are required in constructing these regression models, which are often subjective, prone to overfitting issue, and human errors. This paper proposes a novel physics‐based Environmental‐Effects‐Embedded model updating method. By embedding physical mechanisms of environmental impacts into the formulation of the finite element model, the proposed method is capable of considering these impacts during the finite element model updating. A comparative numerical study is performed by applying both the Environmental‐Effects‐Embedded model updating method and traditional method on a pedestrian bridge model subjected to structural damage, temperature variation, and boundary condition change. Comparison between the proposed and traditional methods has demonstrated that the proposed method can offer more accurate results in localizing and quantifying the structural damage under environmental impacts.

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Section: Numerical Studysupporting

confidence: 87%

Environmental-effects-embedded model updating method considering environmental impacts

Zhou

Song

2017

Struct Control Health Monit

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“…An imprecise damping model may cause large modeling errors. Accordingly, in this study, the adverse effects of damping on model updating results are alleviated through a frequency range selection method that has been applied in several researches to avoid damping matrix updating …”

Section: Proposed Methodsmentioning

confidence: 99%

Transfer function‐based Bayesian damage detection under seismic excitation

Vahedi

Khoshnoudian

Hsu

et al. 2019

Structural Design Tall Build

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Summary Transfer function (TF) data are recognized as diagnostic features in damage detection procedure. The objective of this paper is to present a damage detection method in Bayesian paradigm based on TF data due to ground excitation. The measured seismic responses of the structure in the frequency domain are adopted to obtain displacement TFs and the structural natural frequencies are identified from observed TFs. The derived features are utilized for Bayesian structural damage detection. In addition, the challenging issue of underlying flexible soil in real cases has been addressed. The proposed technique is applied to a numerical shear frame to evaluate the capability of the method. An experimental study on a six‐story steel building has been validated to demonstrate the capability of the method for damage detection purpose. The results of studied cases indicated that the proposed method is capable of identifying the location and the severity of damage precisely.

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“…Using the first‐order Taylor's series and neglecting the higher orders, one obtains

\frac{\partial ϕ_{i}}{\partial p} = \frac{normalΔ ϕ_{i}}{normalΔ p}

\frac{\partial λ_{i}}{\partial p} = \frac{normalΔ λ_{i}}{normalΔ p},

where Δ λ i and Δ ϕ i represent the discrepancies of the i th eigenvalue and eigenvector between the tested structure and the FE model. By substituting Equations and into Equation , yields

ϕ_{i}^{T} (λ_{i} \frac{\partial boldM}{\partial p} normalΔ p + \frac{\partial boldK}{\partial p} normalΔ p) ϕ_{i} = - 2 ϕ_{i}^{T} (λ_{i} boldM + boldK) normalΔ ϕ_{i} - normalΔ λ_{i} .

…”

Section: An Innovative Sensitivity‐based Femu Strategymentioning

confidence: 99%

“…Based on the first‐order Taylor series, one can express

\frac{\partial boldM}{\partial p} normalΔ p = normalΔ boldM,

\frac{\partial boldK}{\partial p} normalΔ p = normalΔ boldK .

…”

Section: An Innovative Sensitivity‐based Femu Strategymentioning

confidence: 99%

A sensitivity‐based finite element model updating based on unconstrained optimization problem and regularized solution methods

Rezaiee‐Pajand

Entezami

Sarmadi

2020

Struct Control Health Monit

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Summary An effective and reliable approach to updating finite element (FE) models of real structures is to utilize a sensitivity‐based strategy. A challenging issue concerning the sensitivity‐based finite element model updating (FEMU) is to create a well‐established framework for updating the inherent structural properties of FE models under incomplete noisy modal data. When noise contaminates the measured modal parameters, another challenging issue stems from the ill‐posedness of the FEMU inverse problem. This article proposes an innovative sensitivity‐based FEMU strategy based on the combination of modal kinetic energy and modal strain energy for simultaneously updating the element mass and stiffness matrices of FE models. The great novelty of this strategy is to get an idea from the unconstrained optimization problem for the establishment of a sensitivity‐based FEMU framework. The correction of the element mass and stiffness matrices in a simultaneous way is another novelty of the proposed FEMU strategy. Moreover, new iterative and hybrid regularization methods under the Krylov subspace theory and bidiagonalization process are presented to solve the ill‐posed inverse problem of FEMU. The accuracy and reliability of the proposed methods are numerically validated by a two‐story concrete frame and a two‐span continuous steel truss along with some comparative analyses. Results demonstrate that the suggested sensitivity‐based strategy and regularized solution methods are influential and successful in FEMU under incomplete noisy modal data.

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Structural model updating using incomplete transfer function and modal data

Cited by 21 publications

References 46 publications

Environmental-effects-embedded model updating method considering environmental impacts

Environmental-effects-embedded model updating method considering environmental impacts

Transfer function‐based Bayesian damage detection under seismic excitation

A sensitivity‐based finite element model updating based on unconstrained optimization problem and regularized solution methods

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