Uncertainty quantification in model verification and validation as applied to large scale historic masonry monuments

Atamturktur, Sez; Hemez, François M.; Laman, Jeffrey A.

doi:10.1016/j.engstruct.2012.05.027

Cited by 57 publications

(25 citation statements)

References 25 publications

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“…Most applications that consider UQ involve model formulations whereby the model requires one forward calculation to obtain the output of interest. There are a vast number of successful studies implementing UQ in the literature, especially for structural dynamics applications pursued with finite element models [17,18]. There are also instances where UQ has been extended to coupled model simulations, however, in many cases assumptions are utilized to justify introducing uncertainty at either the subsystem model level or at the coupled model level, for example in [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Effect of model-form definition on uncertainty quantification in coupled models of mid-frequency range simulations

Buren¹,

Ouisse²,

Cogan³

et al. 2017

Mechanical Systems and Signal Processing

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In the development of numerical models, uncertainty quantification (UQ) can inform appropriate allocation of computational resources, often resulting in efficient analysis for activities such as model calibration and robust design. UQ can be especially beneficial for numerical models with significant computational expense, such as coupled models, which require several subsystem models to attain the performance of a more complex, interconnected system. In the coupled model paradigm, UQ can be applied at either the subsystem model level or the coupled model level. When applied at the subsystem level, UQ is applied directly to the physical input parameters, which can be computationally expensive. In contrast, UQ at the coupled level may not be representative of the physical input parameters, but comes at the benefit of being computationally efficient to implement. To be physically meaningful, analysis at the coupled level requires information about how uncertainty is propagated through from the subsystem level. Herein, the proposed strategy is based on simulations performed at the subsystem level to inform a covariance matrix for UQ performed at the coupled level. The approach is applied to a four-subsystem model of mid-frequency vibrations simulated using the Statistical Modal Energy Distribution Analysis, a variant of the Statistical Energy Analysis. The proposed approach is computationally efficient to implement, while simultaneously capturing information from the subsystem level to ensure the analysis is physically meaningful.

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Section: Literature Reviewmentioning

confidence: 99%

Effect of model-form definition on uncertainty quantification in coupled models of mid-frequency range simulations

Buren¹,

Ouisse²,

Cogan³

et al. 2017

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

show abstract

“…This results in the governing equation for a single degree of freedom oscillator. The steady state frequency response function of an oscillator, |H(ω) i | 2 , to a random, stationary, ergodic and Gaussian pressure spectrum can be computed from equation (24) as shown in equation (25) [19].…”

Section: Structural Responsementioning

confidence: 99%

Section: Structural Responsementioning

confidence: 99%

“…The response function shown in equation (25) represents the transfer function from the input force to the displacement response of the structure. The power spectral density of the displacement of the VTP can be computed from the product of equations (23) and (25). As the joint acceptance is not always unity, Blevins [21] suggests a correction method that requires the inclusion of the joint acceptance.…”

Section: Structural Responsementioning

confidence: 99%

“…These errors are accounted for under the broad topic of model verification. The second source of error in a model arise from uncertainties introduced from an imperfect model definition of underlying physical principles as well as the imprecise values for the associated parameters of the chosen model [25]. Models and their associated parameters can be conditioned based on the experimental data to reduce the uncertainties and infer biases in model predictions.…”

Section: Model Verification Calibration and Validationmentioning

confidence: 99%

See 2 more Smart Citations

Scour monitoring via turbulent open channel flow

Fisher

Khan

Atamturktur

2013

Meas. Sci. Technol.

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Scour is the leading cause of bridge failure in the United States. It can result in the loss of lives and costs millions to repair the damage. A novel method is proposed for monitoring scour that exploits the turbulence in natural channels. The method utilizes the dynamic pressure associated with the turbulent velocity fluctuations in the flow to excite a flexible plate. A semi-empirical model is developed to describe the interaction of turbulent open channel flow with the plate. The model describes the variation of turbulent velocity fluctuations across the flow depth in an open channel resulting in a method for determining the average dynamic pressure on the flexible plate. The dynamic response of the plate is then modeled by superimposing the response of multiple modes of the disk to the random, turbulent dynamic pressure spectrum. The model is verified considering the pressure integration across the plate surface to ensure converged solutions. Due to the uncertainties in the material properties of the plate, the experimentally determined natural frequencies and vibration measurements are used to calibrate the model. The calibrated model predictions are then compared against an independent dataset for validation. In addition to describing the physical operation of the device, the semi-empirical model is also employed to optimize the field device. Measurements made using the field device also confirmed the model results, even in a non-design, misaligned flow condition.

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Applying Uncertainty Quantification to Structural Systems: Parameter Reduction for Evaluating Model Complexity

Locke

Kupis

Gehb

et al. 2019

Model Validation and Uncertainty Quantification, Volume 3

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Uncertainty quantification in model verification and validation as applied to large scale historic masonry monuments

Cited by 57 publications

References 25 publications

Effect of model-form definition on uncertainty quantification in coupled models of mid-frequency range simulations

Effect of model-form definition on uncertainty quantification in coupled models of mid-frequency range simulations

Scour monitoring via turbulent open channel flow

Applying Uncertainty Quantification to Structural Systems: Parameter Reduction for Evaluating Model Complexity

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