Improving Reliability of Markov-based Bridge Deterioration Model using Artificial Neural Network

Abstract

Forecasting long-term performance of bridge by deterioration model is a crucial component in a Bridge Management System (BMS). Markovian-based models are one of the most typical methods to predict long-term bridge performance. The Markovian-based model is selected for predicting bridge deterioration, because it is the most widely accepted prediction model and has been adopted by most State-of-the-Art BMSs. The Markovian-based model is based on transition matrix obtained from overall condition rating of bridges in a network. The change in condition ratings with time provides typical deterioration rates, which can normally be determined from a non-linear regression analysis. Reliable regression analysis requires either large bridge network or sufficient historical condition ratings to obtain accurate transition probability for bridges. Markovian-based model prediction is a simple way to forecast long term performance of individual bridge. However, most bridge agencies do not have adequate condition rating records. This has become a major shortcoming in deterioration modelling. To minimise the abovementioned problem, this paper presents modified Markovian method using previously developed BPM. The BPM is able to generate missing historical condition ratings thereby providing more historical trend of condition depreciation. In this study, BPM-generated condition ratings are used for regression analysis to obtain reliable transition probability required by the Markovian-based model. The results of the proposed study are compared with those of a typical Markovian-based model to identify the advantage of BPM and limitations for further development.