Generating Historical Condition Ratings for the Reliable Prediction of Bridge Deteriorations

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Title Generating Historical Condition Ratings for the Reliable Prediction of Bridge Deteriorations
Author Son, Jung Baeg; Lee, Jaeho; Blumenstein, Michael Myer; Loo, Yew-Chaye; Guan, Hong; Panuwatwanich, Kriengsak
Publication Title Sustainable Infrastructure: Environment Friendly, Safe and Resource Efficient
Editor IABSE
Year Published 2009
Place of publication Thailand
Publisher IABSE
Abstract Bridge Management Systems (BMSs) have been developed since the early 1990s as a decision support system (DSS) for effective Maintenance, Repair and Rehabilitation (MR&R) activities in a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. However, available historical condition ratings are very limited in all bridge agencies. This constitutes the major barrier for predicting reliably future structural performances. To alleviate this problem, the Backward Prediction Model (BPM) technique for generating the missing historical condition ratings has been developed, and its reliability has been verified using existing condition ratings available from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and non-bridge factors including climate, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. Based on these generated datasets, the currently available bridge deterioration model can be used to predict future bridge conditions. The existing 4 National Bridge Inventory and 9 BPM-generated historical condition ratings are used as input data to compare the prediction accuracy using deterministic bridge deterioration models. The comparison results show that prediction error decreases as more historical data become available. This suggests that the BPM can be used to generate additional historical condition ratings, which is essential for bridge deterioration models to achieve more accurate prediction results. However, there are still significant limitations identified in the current bridge deterioration models. Hence, further research is necessary to improve the prediction accuracy of bridge deterioration models.
Peer Reviewed Yes
Published Yes
Publisher URI
Copyright Statement Copyright 2009 IASBE. The attached file is posted here in accordance with the copyright policy of the publisher, for your personal use only. No further distribution permitted. Use hypertext link for access to conference website.
ISBN 9783857481215
Conference name IABSE Symposium 2009
Location Bangkok, Thailand
Date From 2009-09-09
Date To 2009-09-11
Date Accessioned 2010-03-01
Date Available 2015-06-02T05:41:58Z
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 Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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