A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Minimising uncertainty in long-term prediction of bridge element
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Successful bridge management system (BMS) development requires a reliable bridge deterioration model, which is the most crucial component in a BMS. Historical condition ratings obtained from biennial bridge inspections are a major source for predicting future bridge deterioration in BMSs. However, historical condition ratings are very limited in most bridge agencies, thus posing a major barrier for predicting reliable future bridge performance. The purpose of this paper is to present a preliminary study as part of a long-term research on the development of a reliable bridge deterioration model using advanced Artificial Intelligence (AI) techniques.
This proposed study aims to develop a reliable deterioration model. The development work consists of two major Stages: stage 1 – generating unavailable bridge element condition rating records using the Backward Prediction Model (BPM). This helps to provide sufficient historical deterioration patterns for each element; and stage 2 – predicting long-term condition ratings based on the outcome of Stage 1 using time delay neural networks (TDNNs).
Long-term prediction using proposed method can also be expressed in the same form of inspection records – element quantities of each bridge element can be predicted. The proposed AI-based deterioration model does not ignore critical failure risks in small number of bridge elements in low condition states (CSs). This implies that the risk in long-term predictions can be reduced.
The proposed methodology aims to utilise limited bridge inspection records over a short period to predict large datasets spanning over a much longer time period for a reliable, accurate and efficient long-term bridge deterioration model. Typical uncertainty, due to the limitation of overall condition rating (OCR) method, can be minimised in long-term predictions using limited inspection records.
Minimising uncertainty in long-term prediction of bridge element
–
Successful bridge management system (BMS) development requires a reliable bridge deterioration model, which is the most crucial component in a BMS. Historical condition ratings obtained from biennial bridge inspections are a major source for predicting future bridge deterioration in BMSs. However, historical condition ratings are very limited in most bridge agencies, thus posing a major barrier for predicting reliable future bridge performance. The purpose of this paper is to present a preliminary study as part of a long-term research on the development of a reliable bridge deterioration model using advanced Artificial Intelligence (AI) techniques.
This proposed study aims to develop a reliable deterioration model. The development work consists of two major Stages: stage 1 – generating unavailable bridge element condition rating records using the Backward Prediction Model (BPM). This helps to provide sufficient historical deterioration patterns for each element; and stage 2 – predicting long-term condition ratings based on the outcome of Stage 1 using time delay neural networks (TDNNs).
Long-term prediction using proposed method can also be expressed in the same form of inspection records – element quantities of each bridge element can be predicted. The proposed AI-based deterioration model does not ignore critical failure risks in small number of bridge elements in low condition states (CSs). This implies that the risk in long-term predictions can be reduced.
The proposed methodology aims to utilise limited bridge inspection records over a short period to predict large datasets spanning over a much longer time period for a reliable, accurate and efficient long-term bridge deterioration model. Typical uncertainty, due to the limitation of overall condition rating (OCR) method, can be minimised in long-term predictions using limited inspection records.
Minimising uncertainty in long-term prediction of bridge element
Lee, Jaeho (author) / Blumenstein, Michael (author) / Guan, Hong (author) / Loo, Yew-Chaye (author)
Engineering, Construction and Architectural Management ; 20 ; 127-142
2013-02-22
16 pages
Article (Journal)
Electronic Resource
English
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