Multiple repair scenario of life cycle cost of RCC girder bridge using Markov chain model

At present, many road authorities in the world face challenges in condition monitoring diagnosis of distress and forecasting deterioration, strengthening and convalescence of aging bridge structures. The accurate prediction of the future condition is crucial for optimizing the maintenance activities. It is very tough to predict the actual performance scenario or actual in–situ structures without carrying out inspection. Limited availability of detailed inspection data is considered as one of the major drawbacks in developing deterioration models. In State Based Markov deterioration (SNMD) modelling, the main job is to estimate transition probability matrixes (TPMs). In this paper, Markov Chain Monte Carlo (MCMC) is used to estimate TPMs. In Markov Chain Model, future conditions depend on only present bridge inspection data. Multiple repair options are adopted in order to optimize life cycle cost. Repairs are needed when the critical chloride concentration exceeds 0.2. Three distinct types of cost corresponding to each repair option is considered. The objective of this paper is to minimize the life cycle cost considering appropriate repair timings of mixed repair methods. Variation of life cycle cost of five different concretes (stronger to weaker) using three different repair option is shown in this paper. For specific normalized condition of concrete’s failure probability (0.3) and specific type of concrete, variation of life cycle cost using multiple repair options is also shown in this paper.


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