Abstract

AbstractA bridge-deterioration approach is to predict the condition ratings and the deterioration pattern of bridge elements for determining optimal maintenance strategies and estimating future funding requirements. To effectively predict long-term bridge performance, an advanced integrated deterioration approach has been developed that incorporates a time-based model, a state-based model with the Elman neural network (ENN) and a backward prediction model (BPM). The proposed approach involves the categorization of the selected inspection records by bridge components, material types, traffic volume, and the construction era. The primary advantage of such categorization is to group similar components together, thereby identifying the common deterioration patterns. A selection process embedded in the proposed approach offers the ability to automatically select the most appropriate model for predicting future bridge condition ratings. To demonstrate the advantage of the proposed approach in predicting long-te...

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