Bridges are vital components of any road network which demand crucial and timely decision-making for Maintenance, Repair and Rehabilitation (MR&R) activities. Bridge Management Systems (BMSs) as a Decision Support System (DSS), have been developed since the early 1990’s to assist in the management of a large bridge network. Historical condition ratings obtained from biennial bridge inspections are major resources for predicting future bridge 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 and verified to help generate missing historical condition ratings. This is achieved through establishing a correlation between the known condition ratings and such non-bridge factors as climate and environmental conditions, traffic volumes and population growth. Such correlations can then be used to obtain the bridge condition ratings of the missing years. 21 non-bridge factors which were used in initial BPM methodology are refined to 6 non-bridge factors because BPM- generated historical condition rating using this 6 non-bridge factors show good trend with existing condition ratings.

With the help of generated datasets, the currently available bridge deterioration model can be utilised to more reliably forecast future bridge conditions. The prediction accuracies based on 4 and 9 BPM-generated historical condition ratings are compared, using deterministic and stochastic bridge deterioration models. The comparison outcomes indicate that the prediction error decreases as more historical condition ratings become available. This implies that the BPM can be utilised to generate unavailable historical data, which is crucial for bridge deterioration models to achieve more accurate prediction results. Nevertheless, there are still considerable limitations in the existing bridge deterioration models.

In view of this, feasibility study on Time Delay Neural Network (TDNN) using BPM-generated historical condition ratings is conducted as an alternative to existing bridge deterioration models. The proposed TDNN model is capable of solving a number of problems encountered in the existing bridge deterioration models. Thus, it is anticipated that the TDNN using BPM-generated data can lead to further improvement of the current BMS outcome.