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International Journal for Uncertainty Quantification

Publication de 6  numéros par an

ISSN Imprimer: 2152-5080

ISSN En ligne: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

STOCHASTIC SAMPLING BASED BAYESIAN MODEL UPDATING WITH INCOMPLETE MODAL DATA

Volume 6, Numéro 3, 2016, pp. 229-244
DOI: 10.1615/Int.J.UncertaintyQuantification.2016017194
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RÉSUMÉ

In this paper, we are interested in model updating of a linear dynamic system based on incomplete modal data including modal frequencies, damping ratios, and partial mode shapes of some of the dominant modes. To quantify the uncertainties and plausibility of the model parameters, a Bayesian approach is developed. The mass and stiffness matrices in the identification model are represented as a linear sum of the contribution of the corresponding mass and stiffness matrices from the individual prescribed substructures. The damping matrix is represented as a sum of the contribution from individual substructures in the case of viscous damping, in terms of mass and stiffness matrices in the case of classical damping (Caughey damping), or a combination of the viscous and classical damping. A Metropolis-within-Gibbs sampling based algorithm is proposed that allows for an efficient sampling from the posterior probability distribution. The effectiveness and efficiency of the proposed method are illustrated by numerical examples with complex modes.

CITÉ PAR
  1. Garoli Gabriel Y., Pilotto Rafael, Nordmann Rainer, de Castro Helio F., Identification of active magnetic bearing parameters in a rotor machine using Bayesian inference with generalized polynomial chaos expansion, Journal of the Brazilian Society of Mechanical Sciences and Engineering, 43, 12, 2021. Crossref

  2. Bansal Sahil, Cheung Sai Hung, Stochastic Simulation Algorithm for Robust Reliability Updating of Structural Dynamic Systems Based on Incomplete Modal Data, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3, 4, 2017. Crossref

  3. Bansal Sahil, Cheung Sai Hung, On the Bayesian sensor placement for two-stage structural model updating and its validation, Mechanical Systems and Signal Processing, 169, 2022. Crossref

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