Bayesian assessment of an existing bridge - a case study

Jacinto, Luciano; Neves, Lu�s Canhoto; Santos, Lu�s Oliveira

Abstract

This article presents a case study involving the assessment of an existing bridge, starting with simple methods and ending with a probabilistic analysis, the latter emphasising Bayesian methods. When assessing an existing bridge, it is common practice to collect information from the bridge in the form of samples. These samples are in general of small size, raising the question of how the corresponding statistical uncertainty can be taken into account on reliability estimates. The case study illustrates how Bayesian methods are especially suitable to deal with that source of uncertainty. Another strong point of the Bayesian methods is their ability to combine the information contained in the samples collected from the bridge with prior information, if any. This aspect will also be illustrated through the case study.

Citation

Jacinto, L., Neves, L. C., & Santos, L. O. (in press). Bayesian assessment of an existing bridge - a case study. Structure and Infrastructure Engineering, 12(1), https://doi.org/10.1080/15732479.2014.995105

• Journal Article Type: Article
• Acceptance Date: Sep 28, 2014
• Online Publication Date: Jan 6, 2015
• Deposit Date: Jul 27, 2016
• Publicly Available Date: Jul 27, 2016
• Journal: Structure and Infrastructure Engineering
• Print ISSN: 1573-2479
• Electronic ISSN: 1744-8980
• Publisher: Routledge
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 1
• DOI: https://doi.org/10.1080/15732479.2014.995105
• Keywords: Existing bridges; probabilistic assessment; Bayesian updating; statistical uncertainty; predictive model
• Public URL: https://nottingham-repository.worktribe.com/output/743677
• Publisher URL: http://dx.doi.org/10.1080/15732479.2014.995105
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in Structure and Infrastructure Engineering on 06/01/2015, available online: http://www.tandfonline.com/10.1080/15732479.2014.995105.
• Contract Date: Jul 27, 2016