Damage detection using generic elements: Part I. Model updating

Titurus, Branislav; Friswell, Michael I.; Starek, Ladislav

doi:10.1016/s0045-7949(03)00317-1

Cited by 39 publications

(29 citation statements)

References 17 publications

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“…The measured data were obtained using experimental modal analysis [41]. The structure was tested in free-free conditions, by mounting the frame on soft springs, with the response measured by two accelerometers whose locations are shown in Figure 14.…”

Section: Model Description and Problem Definitionmentioning

confidence: 99%

“…The correlation between the modes is ensured by the standard MAC criterion. Titurus et al [41] gave a more detailed description of the experiment, the model and the potential sources of uncertainty. The experimental and analytical natural frequencies are significantly different, indicating inappropriate modelling assumptions that were localized primarily to the joints.…”

Section: Model Description and Problem Definitionmentioning

confidence: 99%

“…The parameterization of the structure is based on the concept of GEs [32], and this particular application is described in [41]. The parameterization is shown in Figure 15, where the 32 elements were divided into four groups of GEs.…”

Section: Parameterization Of the Model Using Gesmentioning

confidence: 99%

“…Furthermore, the correlation for the eighth and ninth modes, not used in the updating, also improves suggesting physically based parameter modifications. Titurus et al [41] updated this structure in a similar way, except they used the regularization p ≈ p 0 . The two approaches give almost identical parameter updates, which further increases confidence in the results.…”

Section: Sensitivity and Updating Analysismentioning

confidence: 99%

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Regularization in model updating

Titurus

Friswell

2007

Numerical Meth Engineering

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SUMMARYThis paper presents the theory of sensitivity-based model updating with a special focus on the properties of the solution that result from the combination of optimization of the response prediction with a priori information about the uncertain parameters. Model updating, together with the additional regularization criterion, is an optimization with two objective functions, and must be linearized to obtain the solution. Structured solutions are obtained, based on the generalized singular value decomposition (GSVD), and specific features of the parameter and response paths as the regularization parameter varies are explored. The four different types of spaces that arise in the solution are discussed together with the characteristics of the regularized solution families. These concepts are demonstrated on a simulated discrete example and on an experimental case study.

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Section: Model Description and Problem Definitionmentioning

confidence: 99%

Section: Model Description and Problem Definitionmentioning

confidence: 99%

Section: Parameterization Of the Model Using Gesmentioning

confidence: 99%

Section: Sensitivity and Updating Analysismentioning

confidence: 99%

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Regularization in model updating

Titurus

Friswell

2007

Numerical Meth Engineering

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“…The structural properties and performance can be assessed with the updated structural model. Model updating can be categorized into deterministic (Titurus et al 2003;Lee et al 2014) and probabilistic (Katafygiotis et al1998;Zhang and Mahadevan 2000;Beck 2010) approaches. Deterministic model updating tries to pinpoint one single solution.…”

Section: Introductionmentioning

confidence: 99%

A feasibility study on the use of bayesian model updating and vibration prediction for structural diagnostic

Lam¹,

Yang²

2015

Proceedings of the Second International Conference on Performance-Based and Life-Cycle Structural Engineering (PLSE 2015)

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This paper puts forward a feasibility study on the use of Bayesian model updating and vibration prediction for structural diagnostic when the level of modeling error is relatively high. The proposed method consists of two parts. In the first part, the Markov chain Monte Carlo (MCMC)-based model updating is employed to calculate the posterior PDF of uncertain model parameters conditional a set of measurement and a given model class. Modeling error is the key problem to be addressed in the practical implementation of structural model updating or damage detection. Apart from very simple structures, model updating of real structures is usually not globally and locally identifiable. Therefore, MCMC simulation is employed in the proposed method in generating samples in the important region(s) for the approximation of the posterior PDF. In the second part, the probabilities for the vibrations of the structure to exceed a list of threshold limits (i.e., the failure probabilities) were calculated using the MCMC samples. It is believed that the failure probabilities for the damaged structure are higher than those for the undamaged one. A 3-dimensional scaled transmission tower model was tested under laboratory conditions for verifying the proposed method. To test the robustness in the detection of damage existence, artificial modelling error was introduced to the model class in the numerical case study. The numerical case study results were positive implying the feasibility of the proposed method.

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Modal–Vibration‐Based Damage Identification

Worden

Friswell

2008

Encyclopedia of Structural Health Monitoring

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A substantial body of structural health monitoring (SHM) research has been carried out in the context of structural dynamics. In this context, it is natural to make use of features for damage identification, which arise in the normal course of structural dynamic testing. The accepted standard for a dynamic test is a modal analysis , which derives a description of the dynamics in terms of natural frequencies, damping ratios, and modeshapes. This article discusses how such features can be used to extract a damage diagnosis. From a modal test, it is possible to extract the physical matrices that enter the structural equations of motion: mass, damping, and stiffness matrices. This article also describes damage identification strategies based on these matrices.

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Damage detection using generic elements: Part I. Model updating

Cited by 39 publications

References 17 publications

Regularization in model updating

Regularization in model updating

A feasibility study on the use of bayesian model updating and vibration prediction for structural diagnostic

Modal–Vibration‐Based Damage Identification

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