Statistical Identification of Structures

Collins, Jon D.; Hart, Gary C.; Hasselman, T. K.; Kennedy, Bruce

doi:10.2514/3.49190

Cited by 386 publications

(87 citation statements)

References 6 publications

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“…The sensitivity maps ⃗ H i now serve as basis functions, which are defined equivalently to the sensitivity maps of the FEMU formulation (Equation (16)). Using the same finite differences approach (17), the FEM simulation of the experiment supplies these maps to the system of Equations (19). This means that the ill-posed DIC problem is now regularized by mechanics that control how material deformation (captured in the images) depends on the constitutive parameters.…”

Section: Integrated Digital Image Correlationmentioning

confidence: 99%

“…where ⃗ F exp (t) is the experimentally measured force and ⃗ F sim (t, i ) is the numerically obtained reaction force, which depends on the constitutive parameters i . This objective function is minimized by optimizing the parameters i according to Gauss -Newton optimization, leading to the following system of equations, analogously to Equations (13) and (19):…”

Section: Load Residualsmentioning

confidence: 99%

“…Additional advantages of full-field measurements include the following: (1) the possibility to identify material parameters using small-scale specimens [1], where traditional techniques for measuring deformation do not suffice, and (2) enhanced accuracy of identified material parameters, due to the use of rich, full-field measurement data.Various (inverse) identification approaches make use of full-field data, such as the virtual fields method [2][3][4], the equilibrium gap method [5][6][7], the reciprocity gap method [8,9], and the constitutive gap method [10][11][12], of which overviews are given in the literature [13][14][15][16][17][18]. Undoubtedly, the most intuitive and widely used method is that of finite element model updating (FEMU) [19][20][21][22]. In this technique, constitutive model parameters are optimized by comparing displacement fields from FE simulation with displacement fields acquired through standard DIC procedures on experimental images.More recently, a novel method has been introduced [23-26], which intimately integrates mechanical descriptions of a material with full-field measurements to identify model parameters.…”

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confidence: 99%

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Comparison of the identification performance of conventional FEM updating and integrated DIC

Ruybalid

Hoefnagels

Sluis

et al. 2015

Int. J. Numer. Meth. Engng

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Summary Full‐field identification methods are increasingly used to adequately identify constitutive parameters to describe the mechanical behavior of materials. This paper investigates the more recently introduced one‐step method of integrated digital image correlation (IDIC) with respect to the most commonly used two‐step method of finite element model updating (FEMU), which uses a subset‐based DIC algorithm. To make the comparison as objective as possible, both methods are implemented in the most equivalent manner and use the same FE model. Various virtual test cases are studied to assess the performance of both methods when subjected to different error sources: (1) systematic errors, (2) poor initial guesses for the constitutive parameters, (3) image noise, (4) constitutive model errors, and (5) experimental errors. Results show that, despite the mathematical similarity of both methods, IDIC produces less erroneous and more reliable results than FEMU, particularly for more challenging test cases exhibiting small displacements, complex kinematics, misalignment of the specimen, and image noise. Copyright © 2015 John Wiley & Sons, Ltd.

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Section: Integrated Digital Image Correlationmentioning

confidence: 99%

Section: Load Residualsmentioning

confidence: 99%

mentioning

confidence: 99%

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Comparison of the identification performance of conventional FEM updating and integrated DIC

Ruybalid

Hoefnagels

Sluis

et al. 2015

Int. J. Numer. Meth. Engng

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“…These are two of the few papers that use Lcurves and generalized cross-validation in model updating. Statistical identification has been applied in References [20,21] using the residuals to estimate some of the statistical properties. These properties are normally difficult to quantify, if the experiments are not repeated.…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection using nonlinear parameter identification with Tikhonov regularization

Weber¹,

Paultre²,

Proulx³

2007

Struct. Control Health Monit.

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A sensitivity‐based update of a finite element model is used to determine stiffness reduction factors from measured eigenfrequencies and mode shapes. This is generally an ill‐posed nonlinear problem that needs some regularization. Tikhonov regularization is combined with iterative linearization. Regularization has to be applied before linearization, otherwise the regularization effect depends strongly on the number of iterations. A nonlinear version of the generalized cross‐validation method is used to determine the optimal regularization parameter. The theory is applied to a two‐dimensional, two‐storey frame. Numerical simulations with random errors are used to demonstrate the effect of regularization. The examples show that regularization can greatly improve the results and that generalized cross‐validation leads to selecting a good regularization parameter. The proposed method is also applied to experimental data obtained from a structure tested in the laboratory at the University of Sherbrooke. Results are in good agreement with experiments, although they depend somewhat on the weighting factors and initial values used for the iterative update. Copyright © 2006 John Wiley & Sons, Ltd.

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“…Several investigators have worked in the area of identification of structural systems so as to extract, from various types of response data, improved characterizations of the systems involved.' - 6 The identification problem can be looked at in terms of a class of inputs I, a class of models M and an error criterion, E. It usually takes the following form: Given the system response (at various locations in a structure) to the class of inputs I, identify a member of the class of models M , which minimizes some error criterion E.…”

Section: Introductionmentioning

confidence: 99%

Non‐parametric identification of a class of non‐linear close‐coupled dynamic systems

Udwadia

Kuo

1981

Earthq Engng Struct Dyn

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A non‐parametric identification technique for the identification of arbitrary memoryless non‐linearities has been presented for a class of close‐coupled dynamic systems which are commonly met with in mechanical and structural engineering. The method is essentially a regression technique and expresses the non‐linearities as series expansions in terms of orthogonal functions. Whereas no limitation on the type of test signals is imposed, the method requires the monitoring of the response of each of the masses in the system. The computational efficiency of the method, its easy implementation on analogue and digital machines and its relative insensitivity to measurement noise make it an attractive approach to the non‐parametric identification problem.

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Statistical Identification of Structures

Cited by 386 publications

References 6 publications

Comparison of the identification performance of conventional FEM updating and integrated DIC

Comparison of the identification performance of conventional FEM updating and integrated DIC

Structural damage detection using nonlinear parameter identification with Tikhonov regularization

Non‐parametric identification of a class of non‐linear close‐coupled dynamic systems

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