ANN-based Structural Performance Model for Reliable Bridge Asset Management
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Bridge Management Systems (BMSs) have been developed to assist in the management of a large bridge network. Historical condition ratings obtained from bridge inspections are major resources for predicting future deteriorations via BMSs. Available historical condition ratings in most bridge agencies, however, are very limited, and thus posing a major barrier for predicting reliable future structural performance. To alleviate this problem, A Backward Prediction Model (BPM) technique has been developed to help generate missing historical condition ratings which is crucial for bridge deterioration models to be able to predict more accurate solutions. Nevertheless, there are still considerable limitations in the existing bridge deterioration models. In view of this, feasibility study of Time Delay Neural Network (TDNN) using BPM-generated historical condition ratings is conducted as an alternative to existing bridge deterioration models. It is anticipated that the TDNN using BPM-generated data can lead to further improvement of the current BMS outcome.
Incorporating Sustainable Practice in Mechanics of Structures and Materials
Copyright 2010 Taylor & Francis. This is the author-manuscript version of the paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the publisher's website for access to the definitive, published version.
Infrastructure Engineering and Asset Management
Simulation and Modelling