GIS-integrated optimization approaches for the culvert maintenance planning and scheduling problem

Abstract

Culvert preservation is essential for extending the lifecycle of these critical infrastructure components. Given the limited annual budgets for maintenance and rehabilitation, it is not feasible to maintain all culverts at optimal performance. Traditional system-level maintenance approaches are often too broad and not cost-effective due to the complexity of culverts, which consist of various components such as barrels, endwalls, junctions, and energy dissipation devices. To address these challenges, this paper proposes a comprehensive framework for optimizing culvert maintenance decisions. A mathematical model and a genetic algorithm are introduced to solve the problem, considering budget limitations, available labor, and other operational constraints. The GIS system is used for extracting spatial information, calculating grouping discounts, and visualizing results. The computational results show that the mathematical model performs well, solving all instances to optimality within seconds. The GA serves as an alternative approach, particularly in cases where a self-contained method is required or where further solution improvement is desired. Using Anderson County as a case study, three key findings are observed: increasing the grouping size for high-requirement jobs improves efficiency, available person-hours significantly impact the choice between in-house and contractor work, and budget increases show diminishing returns after a certain threshold. The findings of this research can help the Tennessee Department of Transportation (TDOT) make more informed and cost-effective decisions regarding culvert maintenance.