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International Journal for Uncertainty Quantification

Published 6 issues per year

ISSN Print: 2152-5080

ISSN Online: 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

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BAYESIAN OPTIMAL EXPERIMENTAL DESIGN INVOLVING MULTIPLE SETUPS FOR DYNAMIC STRUCTURAL TESTING

Volume 9, Issue 5, 2019, pp. 439-452
DOI: 10.1615/Int.J.UncertaintyQuantification.2019025897
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ABSTRACT

In an experimental design for dynamic structural testing, it is a common practice to obtain data from a structure using multiple setups, with each setup covering a different part of the structure. This is generally due to availability of limited number of sensors with synchronous data acquisition. This paper considers the problem of optimal placement of actuators and sensors with the aim of maximizing the data information in an experimental design with multiple setups so that the structural dynamic behavior can be fully characterized. The uncertainty in the model parameters is computed by a Bayesian statistical framework and expected utility is used to quantify the uncertainty of the set of identified model parameters. The problem of optimal experimental design with multiple setups is formulated as an optimization problem in which the actuators' and sensors' configuration, which maximizes the expected utility, is selected as the optimal one. The proposed approach can be used to compare and evaluate the quality of the parameter estimates that can be achieved with different numbers of setups in an experimental design and a different number of actuators and sensors in each setup. The effectiveness of the proposed approach is illustrated by optimal experimental design for a simple 12-degree-of-freedom (dof) chainlike spring-mass model of a structure and a 37-dof truss structure.

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CITED BY
  1. 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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