Optimizing bridge network maintenance management under uncertainty with conflicting criteria: Life-cycle maintenance, failure, and user costs

During the past decade, a variety of bridge maintenance management methodologies have been developed to cost-effectively allocate limited budgets to deteriorating highway bridges for performance enhancement and lifespan extension. In most existing research and practice, however, bridges are treated individually rather than collectively as integral parts of a transportation network. In addition to safety issues, failure of bridges renders a highway network inaccessible, either partially or completely. This may lead to considerable economic consequences, ranging from agency costs caused by repair/reconstruction to user costs as a result of disrupted network service (e.g., congestion and detour). Therefore, it is both natural and important to maintain the satisfactory long-term performance of not only individual bridges but the highway network. In this paper, a novel analytical/computational framework for network-level bridge maintenance management using optimization is presented. This framework integrates time-dependent structural reliability prediction, highway network performance assessment, and life-cycle cost analysis. Failure occurrences of individual bridges and their effects on the overall performance of the highway network are evaluated probabilistically. The maintenance resources are prioritized to deteriorating bridges through simultaneous and balanced minimization of three objective functions, i.e., maintenance cost, bridge failure cost, and user cost. Each of these cost metrics is computed as the present value of the expected economic expenditure accrued over the specified time horizon. The resulting multiobjective optimization problem is solved by a genetic algorithm. An application example is provided for maintenance management of deteriorating reinforced concrete deck slabs of an existing bridge highway network in Colorado. It is shown that the proposed methodology is effective for enhancing the bridge maintenance management practice at network-level through probabilistic quantification and preferred balance of various life-cycle costs.