Finite element model updating using frequency response functions and numerical sensitivities

Sipple, Jesse D.; Sanayei, Masoud

doi:10.1002/stc.1601

Cited by 29 publications

(42 citation statements)

References 22 publications

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“…In the study of 3D truss example 14 elements are considered as measured elements. Among the groups of elements from the SSE view point, elements 1,2,9,10,11,12,14,18,22,27,34,40,42, and 44 are selected. The selection of measurement elements based on the insight provided by SSE (among both groups) as well as the precision of developed sensitivity equation will improve the damage detection process.…”

Section: D Truss Examplementioning

confidence: 99%

“…FRF-based methods provide more data suitable for model updating as compared with modal methods [15][16][17]. In this regard, reference is made to Esfandiari et al [16] who worked on the development of quasi linear sensitivity equation using FRF as metric, Sipple and Sanayei [18] who presented a method based on numerical sensitivities, and Yuan and Yu [19] who conducted two stage model updating based on vibration data of base excitation.…”

Section: Introductionmentioning

confidence: 99%

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Finite element model updating using strain-based power spectral density for damage detection

Pedram

Esfandiari

Khedmati

2016

Struct. Control Health Monit.

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In this paper a new sensitivity-based finite element (FE) model updating method using spectral density of strain as metric is introduced, which is applicable for damage detection and structural health monitoring purposes. Derivation of the sensitivity equation is exact without any need for approximation. A set of incompletely measured natural frequencies and damping loss factors of the damaged structure is used to deal with incomplete measurement without implementation of the FE model reduction or data expansion algorithms. The insights provided from the distribution of the spectral strain energy among the elements in the intact model are used for selection of excitation location. The solution to the developed sensitivity equation is achieved by linear least square and imposing unprejudiced side constraints on the design variables. The proposed method is successfully examined on the FE model of a 3D truss and frame structure and is capable of localization and quantification of damage. The method is robust against measurement, natural frequency, and mass modeling error.

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Section: D Truss Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finite element model updating using strain-based power spectral density for damage detection

Pedram

Esfandiari

Khedmati

2016

Struct. Control Health Monit.

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“…As Figure 11 shows, the proposed approximation of α d (ω) by Eqs. (15) and (16) represents a highly acceptable match with the exact values. Additionally, for the selected frequency ranges described earlier, the approximated formula works well.…”

Section: Applicationmentioning

confidence: 90%

“…Therefore, the initial values of α(ω) and Ω n in Eqs. (15) and (16) are updated iteratively to converge to the corresponding values of damaged structure.…”

Section: Proposed Model Updating Methodsmentioning

confidence: 99%

“…Using FRF data instead of modal data has the advantage of avoiding modal analysis errors, particularly when the extracted modes of structure are close to each other. Utilizing measured FRFs directly to detect structural damage has been studied extensively [8][9][10][11][12][13][14][15]. Using modal data in model updating has the problem of less measured data than unknowns.…”

Section: Introductionmentioning

confidence: 99%

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A frequency response-based structural damage identification using model updating method

Shadan

Khoshnoudian

Esfandiari

2015

Struct. Control Health Monit.

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Summary Structural model updating by estimation of stiffness and mass parameters via monitoring of dynamic characteristics has attracted much attention in recent decades. In this study, frequency response functions (FRF) are utilized in order to identify unknown structural parameters using a sensitivity‐based model updating approach. A sensitivity equation that diminishes adverse effects of incompleteness of FRF data is proposed for model updating. Efficiency of the proposed method and impacts of measurement errors and incompleteness of measured data are examined numerically through a truss reference example. The stiffness and mass parameters of the intact model are updated using the damped FRFs of the simulated damaged model. The results demonstrate that the proposed method is capable of precisely identifying the location and the severity of damage in all studied cases. Copyright © 2015 John Wiley & Sons, Ltd.

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Bayesian model selection considering model complexity using stochastic filtering

Ameri Fard Nasrand,

Mahsuli,

Ghahari

et al. 2023

Earthq Engng Struct Dyn

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This paper proposes a Bayesian model selection framework to determine optimal, parsimonious models for given building structures under ground motion excitation using stochastic filtering. Structural system identification at a regional scale after an earthquake is nominally a monumental task due to its inherently high computational cost. This challenge is currently addressed in the literature by using simplified structural models that bear analytical solutions, such as Timoshenko or shear beams, and shear building models. However, a low computational effort usually leads to an increased prediction error and consequently, a higher model uncertainty. This poses the dilemma of prediction accuracy versus model complexity when selecting a model class to represent a building structure. The proposed framework selects the model class that strikes the best balance between the two. To this end, the notion of cumulative evidence is introduced here as the integral of local evidence over the ground motion duration, which is then formulated as the difference between the cumulative likelihood of the observed measurements and the cumulative penalty. The likelihood measure promotes models whose predictions better match the observations. This is counteracted by the penalty, which is devised as an "Ockham factor," and penalizes models with higher information gains due to their higher complexities, for example, a larger number of parameters. The proposed approach also yields the best tuning parameters of the stochastic filter for each model class and the best initial values of the identification parameters. The proposed framework is verified using a synthetic example, and validated using recorded data from the Millikan Library building in California and the ANX building in Japan.

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Finite element model updating using frequency response functions and numerical sensitivities

Cited by 29 publications

References 22 publications

Finite element model updating using strain-based power spectral density for damage detection

Finite element model updating using strain-based power spectral density for damage detection

A frequency response-based structural damage identification using model updating method

Bayesian model selection considering model complexity using stochastic filtering

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