Bridge annual maintenance prioritization under uncertainty by multiobjective combinatorial optimization

Bridge managers are facing ever-increasing tasks of prioritizing limited budgets to cost-effectively maintain normal functionality of a huge inventory of deteriorating civil infrastructures such as highway bridges over the life cycle. A satisfactory maintenance planning scenario should meet managers' specified requirements for the optimum balance between whole-life costing and structural performance. This article presents a general computational procedure to prioritize on an annual basis maintenance efforts for deteriorating reinforced concrete bridge crossheads over a designated time horizon. Within each year, none or one of the available maintenance types with different performance improvement capabilities could be applied and the time of application for any maintenance intervention is considered to be uniformly distributed within a 1-year time interval. Effects of uncertainties associated with bridge crosshead deterioration processes with and without maintenance interventions are considered by means of Monte Carlo simulation to predict probabilistically structural performance and life-cycle maintenance cost. The resulting combinatorial optimization problem is automated by a multiobjective genetic algorithm. It produces a group of different sequences of annualized maintenance interventions that lead to optimized tradeoff among condition, safety, and life-cycle cost objectives. This enables bridge managers to determine a preferred annual maintenance prioritization solution by comparing different alternatives.