Minimising uncertainty in long-term prediction of bridge element

Purpose - 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. Design/methodology/approach - 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). Findings - 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. Originality/value - 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. © 2012 Emerald Group Publishing Limited