Optimization-based maintenance scheduling for prestressed concrete bridges using Markov chains models. The state of Indiana, U.S.
Road transport infrastructures have high strategic importance for nations’ economic and social development. Bridges are considered as critical assets for road network functionality i.e., any damage can have disastrous social repercussions. However, bridges are one of the most exposed assets; their deterioration can be attributed to material properties, load, and climatic factors, or catastrophic events. As a result, maintenance, rehabilitation, and repairs of existing structures account for about 50% of construction sector spending in most developed nations, and this percentage is projected to rise. Establishing a network-level budget and maintenance schedule is particularly complex due to the heterogeneity of bridge configurations and functions. Deterioration models are established in this sense to determine asset performance and cost-effective and efficient planned maintenance solutions to ensure continuous and correct operation. Markov chains are one of the most used models in this sense, i.e., to assess structure deterioration. The stochastic nature of Markov chains allows for taking into account the uncertainty of complex phenomena as well as their ease of application and compatibility. This study analyzed box beam and girder prestressed concrete bridges in the state of Indiana, U.S. Markov chains degradation models were implemented using National Bridge Inventory data of the last thirty-one years. Annual maintenance costs and budgets were established, and a genetic optimization algorithm was applied to determine the minimum annual maintenance cost for a period of eleven years. The results of the study demonstrate the contribution of the proposed methodology to ensure proper infrastructure maintenance and reduce costs.