Generating Complete Historical Condition Ratings Using a New Set of Non-Bridge Factors for Queensland Bridges in the Backward Prediction Model (BPM)

Abstract

Historical condition ratings obtained from biennial bridge inspections are major resources in a Bridge Management System (BMS) to predict long-term bridge deteriorations. However, such data are very limited in all bridge agencies, making it difficult in obtaining reliable future structural performances. To alleviate this problem, the Backward Prediction Model (BPM) technique for generating the missing historical condition ratings has been developed, and its reliability has been verified using existing condition ratings from the Maryland Department of Transportation, USA. The function of the BPM is to establish the correlations between the known condition ratings and such non-bridge factors as climate and traffic volumes, which can then be used to obtain the bridge condition ratings of the missing years. Since a non-bridge factor can influence the variation of the bridge condition ratings, well-selected non-bridge factors are critical for the BPM to function effectively. In this paper, the composition of non-bridge factors in the existing BPM is refined by excluding insignificant factors while simultaneously adding important ones. This technique produced three groups of non-bridge factors, including 14 factors covering the effects of climate, pollution and heavy traffic volume. These new factors selected for the Queensland environment are found to be effective for the BPM to generate the complete historical condition rating datasets. This in turn improves the reliability in the prediction of future bridge performances.