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  • Improving reliability of bridge deterioration model using generated missing condition ratings

    Author(s)
    Son, Jung
    Lee, Jaeho
    Blumenstein, Michael
    Loo, Yew-Chaye
    Guan, Hong
    Panuwatwanich, Kriengsak
    Griffith University Author(s)
    Loo, Yew-Chaye
    Blumenstein, Michael M.
    Guan, Hong
    Lee, Jaeho
    Panuwatwanich, Kriengsak
    Son, Jung B.
    Year published
    2009
    Metadata
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    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 ...
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    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.
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    Conference Title
    ICCEM-ICCPM 2009
    Publisher URI
    https://www.kicem.or.kr/
    Subject
    Infrastructure Engineering and Asset Management
    Publication URI
    http://hdl.handle.net/10072/31920
    Collection
    • Conference outputs

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