ANN-based structural element performance model for reliable bridge asset management

Publisher:
Taylor & Francis
Publication Type:
Conference Proceeding
Citation:
Incorporating Sustainable Practice in Mechanics of Structures and Materials - Proceedings of the 21st Australian Conference on the Mechanics of Structures and Materials, 2011, pp. 775 - 780
Issue Date:
2011-12-01
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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 ofTime 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. © 2011 Taylor & Francis Group, London.
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