Refinement of backward prediction method for reliable artificial intelligence-based bridge deterioration modelling

Publication Type:
Journal Article
Citation:
Advances in Structural Engineering, 2012, 15 (5), pp. 825 - 836
Issue Date:
2012-05-01
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A deterioration model is the most critical component of a Bridge Management System (BMS). Artificial Intelligence (AI)-based bridge deterioration model has recently been developed to minimise uncertainties in predicting long-term performance of bridge structural elements. 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 elements. However new problems have emerged in the process of TDNN prediction. In this study, the BPM-generated condition ratings are used together with the actual overall condition ratings. The incompatibility between the two sets of data produces unreliable prediction outcomes during the TDNN process. This research therefore aims to introduce a new data processing procedure for BPM outcomes, by removing meaningless condition ratings that cause poor training outcomes for long-term prediction using TDNN. Consequently, the outcome of this study can improve accuracy of the current AI-based bridge deterioration model.
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