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  • ANN-Based Bridge Condition Rating Models Using Limited Structural Inspection Records

    Author(s)
    Lee, Jaeho
    Le, Khoa
    Loo, Yew-Chaye
    Blumenstein, Michael
    Guan, Hong
    Griffith University Author(s)
    Loo, Yew-Chaye
    Blumenstein, Michael M.
    Guan, Hong
    Lee, Jaeho
    Le, Khoa N.
    Year published
    2008
    Metadata
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    Abstract
    The total expenditure for bridge maintenance, repair and rehabilitation (MR&R) and bridge asset extension increases rapidly every year in Australia. Computer-aided Bridge Management Systems (BMSs) are used to establish the best possible bridge MR&R strategies which ensure an adequate level of safety at the lowest possible bridge life-cycle cost. To achieve this, keeping up-to-date bridge information is crucial for a BMS software package. Although most bridge agencies in the past have conducted inspections and maintenance, the format of such bridge inspection records is dissimilar to those required by BMSs. These data ...
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    The total expenditure for bridge maintenance, repair and rehabilitation (MR&R) and bridge asset extension increases rapidly every year in Australia. Computer-aided Bridge Management Systems (BMSs) are used to establish the best possible bridge MR&R strategies which ensure an adequate level of safety at the lowest possible bridge life-cycle cost. To achieve this, keeping up-to-date bridge information is crucial for a BMS software package. Although most bridge agencies in the past have conducted inspections and maintenance, the format of such bridge inspection records is dissimilar to those required by BMSs. These data inconsistencies inhibit correct BMS implementations. This paper presents an Artificial Neural Network (ANN) based prediction model, called the Backward Prediction Model (BPM), for generating historical bridge condition ratings using very limited existing inspection records. The BPM employed historical non-bridge datasets such as traffic volumes, populations and climates, to establish correlations with the existing bridge condition ratings from the very limited bridge inspection records. Such correlations can help fill the condition rating gaps required for an effective and accurate BMS implementation. The outcome of this study can contribute to minimising BMS operational problems due to limited inspection records.
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    Conference Title
    The International Conference on Transport Infrastructure (ICTI2008)
    Publisher URI
    http://www.civil.uminho.pt/ismarti/inicio.htm
    Subject
    Infrastructure Engineering and Asset Management
    Publication URI
    http://hdl.handle.net/10072/24100
    Collection
    • Conference outputs

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