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International Journal for Uncertainty Quantification

年間 6 号発行

ISSN 印刷: 2152-5080

ISSN オンライン: 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

A NEW GIBBS SAMPLING BASED BAYESIAN MODEL UPDATING APPROACH USING MODAL DATA FROM MULTIPLE SETUPS

巻 5, 発行 4, 2015, pp. 361-374
DOI: 10.1615/Int.J.UncertaintyQuantification.2015013581
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要約

This paper presents a new Gibbs sampling based approach for Bayesian model updating of a linear dynamic system based on modal data (natural frequencies and partial mode shapes of some of the dominant modes) obtained from a structure using multiple setups. Modal data from multiple setups pose a problem as mode shapes identified from multiple setups are normalized individually and the scaling factors to form the overall mode shape are not known a priori. For comprehensive quantification of the uncertainties, the proposed approach allows for an efficient update of the probability distribution of the model parameters, overall mode shapes, scaling factors, and prediction error variances. The proposed approach does not require solving the eigenvalue problem of any structural model or matching of model and experimental modes, and is robust to the dimension of the problem. The effectiveness and efficiency of the proposed method are illustrated by simulated numerical examples.

によって引用された
  1. Cheung Sai Hung, Bansal Sahil, A new Gibbs sampling based algorithm for Bayesian model updating with incomplete complex modal data, Mechanical Systems and Signal Processing, 92, 2017. Crossref

  2. Das Ayan, Debnath Nirmalendu, A Bayesian model updating with incomplete complex modal data, Mechanical Systems and Signal Processing, 136, 2020. Crossref

  3. Hızal Çağlayan, Turan Gürsoy, A two-stage Bayesian algorithm for finite element model updating by using ambient response data from multiple measurement setups, Journal of Sound and Vibration, 469, 2020. Crossref

  4. Das Ayan, Debnath Nirmalendu, Limited Sensor-Based Probabilistic Damage Detection Using Combined Normal–Lognormal Distributions, Arabian Journal for Science and Engineering, 46, 5, 2021. Crossref

  5. Das Ayan, Debnath Nirmalendu, Sampling-Based Techniques for Finite Element Model Updating in Bayesian Framework Using Commercial Software, in Advances in Structural Technologies, 81, 2021. Crossref

  6. HIZAL Çağlayan, Sonlu Eleman Modellerinin Maksimum Olasılık Tahmini ile Güncellenmesi, Teknik Dergi, 2021. Crossref

  7. Das Ayan, Debnath Nirmalendu, Gibbs Sampling for Damage Detection Using Complex Modal Data from Multiple Setups, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 7, 2, 2021. Crossref

  8. Bansal Sahil, Bayesian Model Updating Using Modal Data Based on Dynamic Condensation, Journal of Engineering Mechanics, 146, 2, 2020. Crossref

  9. Lyngdoh Gideon Arthur, Rahman Mohammad Arshad, Mishra Sudib Kumar, Bayesian Updating of Structural Model with a Conditionally Heteroscedastic Error Distribution, Journal of Engineering Mechanics, 145, 12, 2019. Crossref

  10. Olalude Oladapo A., Muse Bernard O., Alaba Oluwayemisi O., Informative prior on structural equation modelling with non-homogenous error structure, F1000Research, 11, 2022. Crossref

  11. Olalude Oladapo A., Muse Bernard O., Alaba Oluwayemisi O., Informative prior on structural equation modelling with non-homogenous error structure, F1000Research, 11, 2022. Crossref

  12. Das A., Debnath N., A Bayesian finite element model updating with combined normal and lognormal probability distributions using modal measurements, Applied Mathematical Modelling, 61, 2018. Crossref

  13. Feng Zhouquan, Wang Wenzan, Zhang Jiren, Probabilistic Structural Model Updating with Modal Flexibility Using a Modified Firefly Algorithm, Materials, 15, 23, 2022. Crossref

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