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Visual inspection and bridge management

Author(s)
Quirk, Lucy  
Matos, Jose  
Murphy, Jimmy  
Pakrashi, Vikram  
Uri
http://hdl.handle.net/10197/10351
Date Issued
2018-03-04
Date Available
2019-05-08T12:54:20Z
Abstract
This paper estimates visual inspection quantitatively prior to its implementation in a Bridge Management System using a Value of Information (VoI) approach employing a Bayesian pre-posterior analysis. Information from a significant number of real bridges from Ireland and Portugal are considered in this regard following existing commercial practices. The variation of different parameters on the estimated VoI is investigated including the assumed probabilistic models of the prior bridge state, the likelihood of inspector assigned condition ratings and the economic setting surrounding the cost matrix for maintenance decision alternatives. The values of no information, perfect information and imperfect information are presented and the change in the optimal strategy based on such information is assessed. The effect of human imperfections in assessment and difference in condition rating scale are also estimated. The studies and findings of this paper are expected to allow a better insight for practising engineers and researchers working in bridge management.
Other Sponsorship
COST (European Cooperation in Science and Technology).
Type of Material
Journal Article
Publisher
Taylor & Francis
Journal
Structure and Infrastructure Engineering
Volume
14
Issue
3
Start Page
320
End Page
332
Copyright (Published Version)
2017 Taylor & Francis
Subjects

Bridge maintenance

Visual inspection

Condition rating

Value engineering

Cost estimates

Decision-making

Bayesian Networks

DOI
10.1080/15732479.2017.1352000
Language
English
Status of Item
Peer reviewed
ISSN
1573-2479
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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NSIE-2016-0370.R3-final-auhor-jt.pdf

Size

689.33 KB

Format

Adobe PDF

Checksum (MD5)

7046ccff203c9e17e47e0b64cfe7615c

Owning collection
Mechanical & Materials Engineering Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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