Identification of environmental categories for Markovian deterioration models of bridge decks

In general, state-of-the-art bridge management systems have adopted Markov-chain models to predict the future condition of bridge elements and networks in different environments when various maintenance actions are implemented. However, the categories used to describe the various possible environments for a bridge element are neither accurately defined nor explicitly linked to the external factors affecting the element deterioration. In this paper, a new approach is proposed to provide transportation agencies with an effective decision support tool to identify the categories that best define the environmental and operational conditions specific to their bridge structures. This approach is based on genetic algorithms to determine the combinations of deterioration parameters that best fit each environmental category. The proposed approach is applied to develop Markovian deterioration models for concrete bridge decks using actual data obtained from the Ministére des Transports du Québec. This application illustrates the ability of the proposed approach to correlate the definition of environmental categories to parameters, such as highway class, region, average daily traffic, and percentage of truck traffic, in an accurate and efficient manner.