Prediction of Long-Term Bridge Performance: Integrated Deterioration Approach with Case Studies
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Deterioration approach is used to predict the condition ratings of bridge elements for determining optimal maintenance strategies and estimating future funding requirements. To effectively predict long-term bridge performance, an advanced integrated deterioration method has been developed which incorporates a time-based model, a state-based model with the Elman Neural Network (ENN) and a Backward Prediction Model (BPM). The proposed method involves the categorisation of the selected inspection records by bridge components, material types, traffic volume and the construction era. The main advantage of such categorisation is to group similar components together, thereby identifying the common deterioration patterns. A selection process embedded in the proposed method offers the ability to automatically select the most appropriate model for predicting future bridge condition ratings. To demonstrate the advantage of the proposed method in predicting long-term bridge performances, case studies are performed using the New York State inspection records available from the U.S. National Bridge Inventory (NBI) database. To compare the performance of the proposed method against the standard Markovian-based deterioration procedure in predicting future bridge condition ratings, a total of 40 bridges with 464 bridge substructure inspection records are selected and used as input. The predicted outcomes are validated by a cross-validation process, which demonstrates that the proposed method is more accurate than the standard Markovian-based procedure.
Journal of Performance of Constructed Facilities, ASCE.
© 2014 American Society of Civil Engineers (ASCE). 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.