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Performance evaluation of corrosion-affected reinforced concrete bridge girders using Markov chains with fuzzy states

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Abstract

A methodology for performance evaluation of reinforced concrete bridge girders in corrosive environments is proposed. The methodology uses the concept of performability and considers both serviceability- and ultimate-limit states. The serviceability limit states are defined based on the degree of cracking (characterized by crack width) in the girder due to chloride induced corrosion of reinforcement, and the ultimate limit states are defined based on the flexural load carrying capacity of the girder (characterized in terms of rating factor using the load and resistance factor rating method). The condition of the bridge girder is specified by the assignment of a condition state from a set of predefined condition states. Generally, the classification of condition states is linguistic, while the condition states are considered to be mutually exclusive and collectively exhaustive. In the present study, the condition states of the bridge girder are also represented by fuzzy sets to consider the ambiguities arising due to the linguistic classification of condition states. A non-homogeneous Markov chain (MC) model is used for modeling the condition state evolution of the bridge girder with time. The usefulness of the proposed methodology is demonstrated through a case study of a severely distressed beam of the Rocky Point Viaduct. The results obtained using the proposed approach are compared with those obtained using conventional MC model. It is noted that the use of MC with fuzzy states leads to conservative decision making for the problem considered in the case study.

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This paper is being published with the kind permission of the Director, CSIR-SERC, Chennai.

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  1. CSIR-Structural Engineering Research Centre, CSIR Campus, Taramani, Chennai, 600 113, India

    M B Anoop & K Balaji Rao

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Anoop, M.B., Balaji Rao, K. Performance evaluation of corrosion-affected reinforced concrete bridge girders using Markov chains with fuzzy states. Sādhanā 41, 887–899 (2016). https://doi.org/10.1007/s12046-016-0518-3

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