Semi-Markovian Maintenance Optimization for Reinforced Concrete Enabled by a Synthesized Deterioration Model

Aging bridge infrastructure may jeopardize safety, and its economic impact has raised concerns worldwide. Reinforced concrete is a major component of bridge infrastructure, whose condition is directly related to reliability and safety. However, evaluating and maintaining reinforced concrete remains a complex issue, as it is often exposed to a dynamic and stochastic environment. In this article, we provide an integrated approach to capturing the characteristics of reinforced concrete deterioration by synthesizing knowledge from the physical model and a stochastic model. Then, a semi-Markov decision process is constructed for optimizing the maintenance of reinforced concrete. To improve the computation efficiency of the approach, we design the policy iteration algorithm with finite evaluations to reduce computation cost. The designed approach can further calibrate the temporal and complex dynamic information of the reinforced concrete deterioration and has a better performance than that of the Markov decision process. Finally, we explore the sensitivity of the maintenance policy under the changing of cost rates and durations of the maintenance activities.