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  • Modelling Long-term Bridge Deterioration at Structural Member Level Using Artificial Intelligence Techniques

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    Author(s)
    Lee, Jaeho
    Guan, Hong
    Loo, Yew-Chaye
    Blumenstein, Michael
    Wang, Xin-ping
    Griffith University Author(s)
    Loo, Yew-Chaye
    Blumenstein, Michael M.
    Guan, Hong
    Lee, Jaeho
    Year published
    2011
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    Abstract
    Efficient use of public funds for structural integrity of bridge networks requires an effective bridge asset management technology. To achieve this, a reliable deterioration model is essential in any Bridge Management System (BMS). The deterioration rate is calculated based on historical condition ratings obtained from the structural element-level bridge inspections. Although most bridge authorities have previously conducted inspection and maintenance tasks, these past inspection records are incompatible with what are required by a typical BMS as input. Such incompatibility is a major cause for the deficiency of the current ...
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    Efficient use of public funds for structural integrity of bridge networks requires an effective bridge asset management technology. To achieve this, a reliable deterioration model is essential in any Bridge Management System (BMS). The deterioration rate is calculated based on historical condition ratings obtained from the structural element-level bridge inspections. Although most bridge authorities have previously conducted inspection and maintenance tasks, these past inspection records are incompatible with what are required by a typical BMS as input. Such incompatibility is a major cause for the deficiency of the current BMS outcomes. Artificial Intelligence (AI)-based bridge deterioration model has recently been developed to minimise uncertainties in predicting deterioration of structural bridge members (e.g. beams, piers etc). This model contains two components: (1) using Neural Network-based Backward Prediction Model (BPM) to generate unavailable historical condition ratings; and (2) using Time Delay Neural Network (TDNN) to perform long-term performance prediction of bridge structural members. However new problems have emerged in the process of TDNN prediction. This is because the BPM-generated condition ratings are used together with the actual condition ratings. The incompatibility between the two sets of data produces unreliable prediction outcomes during the TDNN process. This research is thus to develop a new process based on the existing method, thereby overcoming the abovementioned problems. To achieve this, the actual overall condition ratings are replaced by the BPM forward predicted condition ratings. Consequently, the outcome of this study can improve accuracy of long-term bridge deterioration prediction.
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    Journal Title
    Applied Mechanics and Materials
    Volume
    99-100
    DOI
    https://doi.org/10.4028/www.scientific.net/AMM.99-100.444
    Copyright Statement
    © 2011 Trans Tech Publications. 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.
    Subject
    Infrastructure Engineering and Asset Management
    Engineering
    Publication URI
    http://hdl.handle.net/10072/43609
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    • Journal articles

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