Abstract:
Condition monitoring diagnosis of distress and forecasting deterioration, strengthening and rehabilitation of aging bridge structures is a challenge faced by many road authorities in the world. The accurate prediction of the future condition of bridge elements is essential for optimising the maintenance activities. Most authorities conduct regular condition inspection activities followed by a higher level inspection to diagnose specific distress mechanisms. However, network level modelling utilizing condition data to predict the future condition of bridges is a need identified by bridge asset managers. In developing deterioration models for bridges, one of the major drawbacks is the limited availability of detailed inspection data. Condition data collected using discrete condition rating schemes most of the time are inadequate to develop deterministic deterioration models. Among the reliability based models which can be derived using limited condition data, Markov models have been used extensively in modelling the deterioration of infrastructure facilities. These models can predict the conditions of bridge elements as a probabilistic estimate. This paper presents an approach used in the prediction of future condition of reinforced concrete and timber bridge elements using a stochastic Markov chain model. Condition data obtained from two local councils in Victoria, Australia has been used in derivation of the models.
