Comprehensive Bayesian structural identification using temperature variation

Jesus, Andre; Brommer, Peter; Zhu, Yanjie; Laory, Irwanda

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.

Citation

Jesus, A., Brommer, P., Zhu, Y., & Laory, I. (2017). Comprehensive Bayesian structural identification using temperature variation. Engineering Structures, 141, 75-82. https://doi.org/10.1016/j.engstruct.2017.01.060

• Journal Article Type: Article
• Acceptance Date: Jan 25, 2017
• Online Publication Date: Mar 19, 2017
• Publication Date: Jun 15, 2017
• Deposit Date: Sep 26, 2019
• Journal: Engineering Structures
• Print ISSN: 0141-0296
• Electronic ISSN: 1873-7323
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 141
• Pages: 75-82
• DOI: https://doi.org/10.1016/j.engstruct.2017.01.060
• Public URL: https://uwe-repository.worktribe.com/output/2485961
• Publisher URL: https://doi.org/10.1016/j.engstruct.2017.01.060
• Additional Information: This article is maintained by: Elsevier; Article Title: Comprehensive Bayesian structural identification using temperature variation; Journal Title: Engineering Structures; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.engstruct.2017.01.060; Content Type: article; Copyright: © 2017 Elsevier Ltd. All rights reserved.