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Comprehensive Bayesian structural identification using temperature variation

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Jesus, André H., Brommer, Peter, Zhu, Yanjie and Laory, Irwanda (2017) Comprehensive Bayesian structural identification using temperature variation. Engineering Structures, 141 . pp. 75-82. doi:10.1016/j.engstruct.2017.01.060

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Official URL: https://doi.org/10.1016/j.engstruct.2017.01.060

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Abstract

A modular Bayesian method is applied for structural identification of a reduced-scale aluminium bridge model subject to thermal loading. The deformation and temperature variations of the structure were measured using strain gauges and thermocouples. Feasibility of a practical, temperature-based, Bayesian structural identification is highlighted. This methodology used multiple responses to identify existent discrepancies of a model, calibrate the stiffness of the bridge support and establish uncertainty of a predicted response. Results show that the inference of a structural parameter is successful even in the presence of substantial modelling discrepancies, converging to its true physical value. However measurements should have a high dependency on the calibration parameters. Usage of temperature variations to perform structural identification is highlighted.

Item Type: Journal Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH): Structural analysis (Engineering), Structural analysis (Engineering) --Mathematical models., Bridges -- Design and construction.
Journal or Publication Title: Engineering Structures
Publisher: Elsevier Science Ltd.
ISSN: 0141-0296
Official Date: 15 June 2017
Dates:
DateEvent
15 June 2017Published
19 March 2017Available
25 January 2017Accepted
Volume: 141
Page Range: pp. 75-82
DOI: 10.1016/j.engstruct.2017.01.060
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Engineering and Physical Sciences Research Council (EPSRC)
Grant number: 1568046

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