Improving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM)

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Title Improving the reliability of a Bridge Management System (BMS) using an ANN-based Backward Prediction Model (BPM)
Author Lee, Jaeho; Kamalarasa, Kamalarasa; Blumenstein, Michael Myer; Loo, Yew-Chaye
Journal Name Automation in Construction
Editor M.J. Skibniewski
Year Published 2008
Place of publication UK
Publisher Elsevier
Abstract The slow adoption of Bridge Management Systems (BMSs) and its impractical future prediction of the condition rating of bridges are attributed to the inconsistency between BMS inputs and bridge agencies' existing data for a BMS in terms of compatibility and the enormous number of bridge datasets that include historical structural information. Among these, historical bridge element condition ratings are some of the key pieces of information required for bridge asset prioritisation but in most cases only limited data is available. This study addresses the abovementioned difficulties faced by bridge management agencies by using limited historical bridge inspection records to model time-series element-level data. This paper presents an Artificial Neural Network (ANN) based prediction model, called the Backward Prediction Model (BPM), for generating historical bridge condition ratings using limited bridge inspection records. The BPM employs historical non-bridge datasets such as traffic volumes, populations and climates, to establish correlations with existing bridge condition ratings from very limited bridge inspection records. The resulting model predicts the missing historical condition ratings of individual bridge elements. The outcome of this study can contribute to reducing the uncertainty in predicting future bridge condition ratings and so improve the reliability of various BMS analysis outcomes.
Peer Reviewed Yes
Published Yes
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Copyright Statement Copyright 2008 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
Volume 17
Issue Number 6
Page from 758
Page to 772
ISSN 0926-5805
Date Accessioned 2009-03-09
Date Available 2015-06-02T05:40:29Z
Language en_US
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Infrastructure Engineering and Asset Management
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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