On the use of flexural wave propagation experiments for identification of complex modulus

Mahata, Kaushik; Mousavi, Saed; Söderström, Torsten; Mossberg, Magnus; Valdek, Urmas; Hillström, Lars

doi:10.1109/tcst.2003.819589

Cited by 17 publications

(26 citation statements)

References 10 publications

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“…One approach is based on the nonparametric estimate described in for example [8,10], while the other two fit the parametric model directly from the measured data. In all three cases, a least squares type loss function Â( ) is used, and an estimate of the parameter vector is found throughˆ = arg min Â( )…”

Section: Parametric Identificationmentioning

confidence: 99%

“…In [11], an approach to fit a parametric model based on the nonparametric estimate in [8,10] is described. The loss function is in this case given by…”

Section: Parametric Identification Using the Nonparametric Estimatementioning

confidence: 99%

“…In (8),ˆ ( ) is the nonparametric estimate in [8,10] and ( ) is the complex modulus model. Furthermore, the weighting ( ) in (7) is chosen as the variance of the nonparametric estimate, i.e.…”

Section: Parametric Identification Using the Nonparametric Estimatementioning

confidence: 99%

“…The complex modulus of a viscoelastic material can be identified both nonparametrically, as in [5,8,10], or parametrically, as in [11,12,16]. In nonparametric identification, the value of the complex modulus is determined at a number of discrete frequency points .…”

Section: Introductionmentioning

confidence: 99%

“…Two of these have been used in previous studies, see for example [11,10]. In the first approach, the parametric model is fitted from a nonparametric model described in, for example, [8], while in the second approach the model is fitted from the measured strain data directly. For the sake of the coming analysis these approaches are here presented in more detail and the accuracy of the estimates analyzed.…”

Section: Introductionmentioning

confidence: 99%

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Parametric identification of complex modulus

Rensfelt

Söderström

2011

Automatica

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This report treats three different approaches to parametric identification of the complex modulus of a viscoelastic material. In the first approach, a nonparametric estimate is used to fit the parametric model, while in the other two the model is fitted directly from data. In all three cases, theoretical expressions for the accuracy of the estimate are derived under the assumption that the measurement noise is white and that the signal-to-noise ratio is large. The expressions are validated against both simulated and experimental data. In the case of experimental data it is seen that the theoretical expression severely underestimates the variance of the identified parameters, and one of the expressions is therefore modified to cover the case of correlated noise with much better agreement as a result.

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Section: Parametric Identificationmentioning

confidence: 99%

“…In [11], an approach to fit a parametric model based on the nonparametric estimate in [8,10] is described. The loss function is in this case given by…”

Section: Parametric Identification Using the Nonparametric Estimatementioning

confidence: 99%

Section: Parametric Identification Using the Nonparametric Estimatementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parametric identification of complex modulus

Rensfelt

Söderström

2011

Automatica

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Viscoelastic Materials

Creep Mechanics

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Viscoelastic materials can today be found in a wide range of practical applications. In order to make efficient use of these materials in construction, it is of importance to know how they behave when subjected to dynamic load. Characterization of viscoelastic materials is therefore an important topic, that has received a lot of attention over the years.This thesis treats different nonparametric methods for identifying the complex modulus of an viscoelastic material. The complex modulus is a frequency dependent material function, that describes the deformation of the material when subjected to uniaxial stress. With knowledge about this and other material functions, it is possible to simulate and predict how the material behaves under different kinds of dynamic loads. The complex modulus is often identified through wave propagation testing.An important aspect of identification is the accuracy of the estimates. For the identification to be as accurate as possible, it is important that the experimental data contains as much valuable information as possible. Different experimental condition, such as sensor locations and choice of excitation, can influence the amount of valuable information in the data. The procedure of determining optimal values for such design parameters is known as optimal experiment design.The first two papers of the thesis treats optimal experiment design for nonparametric identification of the complex modulus, based on wave propagation tests on large homogenous specimens. Optimal sensor locations is treated in the first paper, and optimal excitation in the second. In the third paper, a technique for estimating the complex modulus for a small pelletsized specimen is presented. Three different procedures are considered, and an analysis of the accuracy of the estimates is carried out. List of PublicationsThis thesis is based on the following publications: A version can be found in:A. Rensfelt and T. Söderström. Optimal excitation for nonparametric identification of viscoelastic materials.

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