Inverse propagation of uncertainties in finite element model updating through use of fuzzy arithmetic

Erdoğan, Yıldırım Serhat; Bakır, Pelin Gündeş

doi:10.1016/j.engappai.2012.10.003

Cited by 43 publications

(9 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Khodaparast et al employed a Kriging predictor as a meta-model for the updating of interval [15] and fuzzy [16] finite element models, based on the minimisation of the difference between simulation and measurement data. Erdogan and Bakir used a hybrid combination of a Genetic Algorithm and Particle Swarm as updating scheme for the minimisation of the difference between measurement and simulation data in the context of the updating of Fuzzy Finite Element models [17]. Fedele et al used an adjoint optimisation technique to estimate the bounds of a model parameter, based on uncertain raw measurements [18].…”

Section: Problem Statement Literature Review and General Overviewmentioning

confidence: 99%

Identification and quantification of multivariate interval uncertainty in finite element models

Faes

Cerneels

Vandepitte

et al. 2017

Computer Methods in Applied Mechanics and Engineering

View full text Add to dashboard Cite

The objective of this work is to develop and validate a methodology for the identification and quantification of multivariate interval uncertainty in finite element models. The principal idea is to find a solution to an inverse problem, where the variability on the output side of the model is known from measurement data, but the multivariate uncertainty on the input parameters is unknown. For this purpose, the uncertain simulation results set created by propagating interval uncertainty through the model is represented by its convex hull. The same concept is used to model the uncertainty in the measurements. A metric to describe the discrepancy between these convex hulls is defined based on the difference between their volumes and their mutual intersection. By minimisation of this metric, the interval uncertainty on the input side of the model is identified. It is further shown how the procedure can be optimized with respect to output quantity selection. Validation of the methodology is done using simulated measurement data in two case studies. Numerically exact identification of multiple, coupled parameters having interval uncertainty is possible following the proposed methodology. Furthermore, the robustness of the method with respect to the analysts initial estimate of the input uncertainty is illustrated. The method presented in this work in se is generic, but for the examples in this paper, it is specifically applied to dynamic models, using eigenfrequencies as output quantities, as

show abstract

Section: Problem Statement Literature Review and General Overviewmentioning

confidence: 99%

Identification and quantification of multivariate interval uncertainty in finite element models

Faes

Cerneels

Vandepitte

et al. 2017

Computer Methods in Applied Mechanics and Engineering

View full text Add to dashboard Cite

show abstract

“…Based on this principle, the interval bounds of the output can be obtained. The original solution procedure for IFEA is the interval arithmetic approaches [7][8][9][10], in which all basic deterministic algebraic operations are replaced by their interval arithmetic counterparts.…”

Section: Arithmetic Approachmentioning

confidence: 99%

State‐of‐the‐Art Nonprobabilistic Finite Element Analyses

Wang¹,

Zhiping²,

Yan³

2017

Uncertainty Quantification and Model Calibration

View full text Add to dashboard Cite

The finite element analysis of a mechanical system is conventionally performed in the context of deterministic inputs. However, uncertainties associated with material properties, geometric dimensions, subjective experiences, boundary conditions, and external loads are ubiquitous in engineering applications. The most popular techniques to handle these uncertain parameters are the probabilistic methods, in which uncertainties are modeled as random variables or stochastic processes based on a large amount of statistical information on each uncertain parameter. Nevertheless, subjective results could be obtained if insufficient information unavailable and nonprobabilistic methods can be alternatively employed, which has led to elegant procedures for the nonprobabilistic finite element analysis. In this chapter, each nonprobabilistic finite element analysis method can be decomposed as two individual parts, i.e., the core algorithm and preprocessing procedure. In this context, four types of algorithms and two typical preprocessing procedures as well as their effectiveness were described in detail, based on which novel hybrid algorithms can be conceived for the specific problems and the future work in this research field can be fostered.

show abstract

“…In the context of quantifying scalar interval uncertainty, to date most presented methods employ a squared L 2 -norm based objective function that is aimed at minimising the discrepancy of the separate interval boundaries of respectively a measurement data set and the prediction of the interval FE model(see e.g. [12,13,14,15]). An alternative method was introduced by Khodaparast et al who used a Kriging predictor for the inverse propagation of deterministic measurement points in order to estimate the hypercubic interval uncertainty on the input parameters of the model under consideration [16].…”

Section: Introductionmentioning

confidence: 99%

Identification and quantification of spatial interval uncertainty in numerical models

Faes

Moens

2017

Computers & Structures

View full text Add to dashboard Cite

Inverse propagation of uncertainties in finite element model updating through use of fuzzy arithmetic

Cited by 43 publications

References 32 publications

Identification and quantification of multivariate interval uncertainty in finite element models

Identification and quantification of multivariate interval uncertainty in finite element models

State‐of‐the‐Art Nonprobabilistic Finite Element Analyses

Identification and quantification of spatial interval uncertainty in numerical models

Contact Info

Product

Resources

About