Dynamic Bayesian updating approach for predicting bridge condition based on Indonesia-bridge management system (I-BMS)

Abstract

Bridges are one of the most important infrastructures which support the transportation system. It requires continuous monitoring to keep its condition and functionality. Bridge monitoring is used to support the maintenance strategy in order to prevent deterioration and sudden failure. This paper aims to propose a probabilistic prediction model of bridge conditions based on the Dynamic Bayesian Updating Approach. Around 3.166 data of bridges in Indonesia were collected from the Directorate of Bridges of the Ministry of Public Works and Housing for calculating the conditional probability table (CPT) of the model. A medium-span concrete bridge was used as a case study to validate the proposed model. The results show that the proposed model can predict the condition of the bridge accurately. It also can be used as an early warning system in order to prevent disasters due to technology failure.