Improving reliability of bridge deterioration model using generated missing condition ratings

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Title Improving reliability of bridge deterioration model using generated missing condition ratings
Author Son, Jung Baeg; Lee, Jaeho; Blumenstein, Michael Myer; Loo, Yew-Chaye; Guan, Hong; Panuwatwanich, Kriengsak
Publication Title ICCEM-ICCPM 2009
Editor Soon Wook Kwan
Year Published 2009
Place of publication Jeju, South Korea
Publisher KICEM (Korea Institute of Construction Engineering and Management)
Abstract Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a decision support system (DSS), have been developed since the early 1990’s to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for obtaining reliable future structural performances. To alleviate this problem, the verified Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings. This is achieved through establishing the correlation between known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. With the help of these generated datasets, the currently available bridge deterioration model can be utilized to more reliably forecast future bridge conditions. In this paper, the prediction accuracy based on 4 and 9 BPM-generated historical condition ratings as input data are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings obtained. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are considerable limitations in the existing bridge deterioration models. Thus, further research is essential to improve the prediction accuracy of bridge deterioration models.
Peer Reviewed Yes
Published Yes
Publisher URI
Conference name ICCEM-ICCPM 2009
Location Jeju, South Korea
Date From 2009-05-27
Date To 2009-05-30
Date Accessioned 2010-03-01
Date Available 2015-06-02T05:43:32Z
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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