Improved bridge evaluation through finite element model updating using static and dynamic measurements

Schlune, Hendrik; Plos, Mario; Gylltoft, Kent

doi:10.1016/j.engstruct.2009.02.011

Cited by 143 publications

(69 citation statements)

References 24 publications

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“…Since then, there has been an increasing popularity for Bayesian-updating methodologies [3,4]. Many applications of dynamic monitoring of bridges can be found in the literature, for example [3,9,13,14,23,26,31,33,38,40,41,46,49]. Some of these studies, such as [3] and [14], explicitly included uncertainties in the identification process.…”

Section: Introductionmentioning

confidence: 99%

Hybrid probabilities and error-domain structural identification using ambient vibration monitoring

Goulet

Michel²,

Smith

2013

Mechanical Systems and Signal Processing

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For the assessment of structural behavior, many approaches are available to compare model predictions with measurements. However, few approaches include uncertainties along with dependencies associated with models and observations. In this paper, an error-domain structural identification approach is proposed using ambient vibration monitoring (AVM) as the input. This approach is based on the principle that in science, data cannot truly validate an hypothesis, it can only be used to falsity it. Error-domain model falsification generates a space of possible model instances (combination of parameters), obtains predictions for each of them and then, rejects instances that have unlikely differences (residuals) between predictions and measurements. Models are filtered in a two step process. Firstly a comparison of mode shapes based on MAC criterion ensures that the same modes are compared. Secondly, the frequencies from each model instance are compared with the measurements. The instances for which the difference between the predicted and measured value lie outside threshold bounds are discarded. In order to include "uncertainty of uncertainty" in the identification process, a hybrid probability scheme is also presented. The approach is used for the identification of the Langensand Bridge in Switzerland. It is used to falsify the hypothesis that the bridge was behaving as designed when subjected to ambient vibration inputs, before opening to the traffic. Such small amplitudes may be affected by lowlevel bearing-device friction. This inadvertently increased the apparent stiffness of the structure by 17%. This observation supports the premiss that ambient vibration surveys

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Section: Introductionmentioning

confidence: 99%

Hybrid probabilities and error-domain structural identification using ambient vibration monitoring

Goulet

Michel²,

Smith

2013

Mechanical Systems and Signal Processing

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“…In another experimental and analytical study of a historic railway bridge by Spyrakos et al [2], seismic and wind load carrying capacities of the bridge were evaluated based on the analytical model validated with static and dynamic field measurements and laboratory tests. Schlune et al [3] proposed a methodology for finite element model updating for improved bridge evaluation and this methodology was applied to one of the world's largest single-arch bridges. For numerous existing small and medium single span ballasted railway bridges in Austria, dynamic field measurements were performed by Rebelo et al [4] and the calibration of the finite element * Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a steel railway bridge for dynamic and seismic loads

Caglayan

Özakgül

Tezer

et al. 2011

Journal of Constructional Steel Research

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“…An alternative is to apply a multi-objective optimization scheme, which allows for the minimization of multiple objective functions simultaneously, thereby avoiding the challenging task of weighting the individual (and possibly contradictory) objectives. For instance, Haralampidis et al [34] and Christodoulou and Papadimitriou [35] define separate eigenfrequency and mode shape residuals, Jaishi and Ren [30] use the approach to incorporate both eigenfrequency and strain energy residuals, and Schlune et al [36] combine static and dynamic measurements in multiple objectives. A different approach to the modal updating problem in structural dynamics was developed by Ladevèze and his co-workers [17][18][19][20].…”

Section: Cost Functionmentioning

confidence: 99%

Dealing with uncertainty in model updating for damage assessment: A review

Simoen

Roeck

Lombaert

2015

Mechanical Systems and Signal Processing

383

217

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In structural engineering, model updating is often used for non-destructive damage assessment: by calibrating stiffness parameters of finite element models based on experimentally obtained (modal) data, structural damage can be identified, quantified and located. However, the model updating problem is an inverse problem prone to ill-posedness and ill-conditioning. This means the problem is extremely sensitive to small errors, which may potentially detract from the method's robustness and reliability. As many errors or uncertainties are present in model updating, both regarding the measurements as well as the employed numerical model, it is important to take these uncertainties suitably into account. This paper aims to provide an overview of the available approaches to this end, where two methods are treated in detail: a non-probabilistic fuzzy approach and a probabilistic Bayesian approach. These methods are both elaborated for the specific case of vibration-based finite element model updating for damage assessment purposes.

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Improved bridge evaluation through finite element model updating using static and dynamic measurements

Cited by 143 publications

References 24 publications

Hybrid probabilities and error-domain structural identification using ambient vibration monitoring

Hybrid probabilities and error-domain structural identification using ambient vibration monitoring

Evaluation of a steel railway bridge for dynamic and seismic loads

Dealing with uncertainty in model updating for damage assessment: A review

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