🤖 AI Summary
Existing pavement maintenance prioritization models consider only pavement condition, cost, and budget constraints, neglecting the spatial distribution of projects—leading to geographically dispersed work sites and inefficient field supervision. This paper proposes the first maintenance decision-making framework that embeds spatial clustering into a budget-constrained, Pavement Condition Index (PCI)-driven optimization model. Integrating GIS coordinate analysis, a custom spatial clustering algorithm, and a multi-objective optimization formulation, the method enables spatiotemporally coordinated, centralized construction scheduling. Evaluated on 1,800 real-world projects across Milton, Georgia, and Tyler, Texas, it significantly reduces equipment mobilization costs while improving on-site supervision efficiency and crew collaboration. The core contribution is the formal incorporation of geographic proximity as a primary decision criterion—marking a paradigm shift from conventional non-spatial optimization approaches—and providing a scalable, methodology-driven foundation for intelligent pavement asset management.
📝 Abstract
To date, pavement management software products and studies on optimizing the prioritization of pavement maintenance and rehabilitation (MR the pre-treatment pavement condition, the rehabilitation cost, and the available budget. Yet, the role of the candidate projects' spatial characteristics in the decision-making process has not been deeply considered. Such a limitation, predominately, allows the recommended M&R projects' schedule to involve simultaneously running but spatially scattered construction sites, which are very challenging to monitor and manage. This study introduces a novel approach to integrate pavement segments' spatial coordinates into the M&R prioritization analysis. The introduced approach aims at combining the pavement segments with converged spatial coordinates to be repaired in the same timeframe without compromising the allocated budget levels or the overall target Pavement Condition Index (PCI). Such a combination would result in minimizing the routing of crews, materials and other equipment among the construction sites and would provide better collaborations and communications between the pavement maintenance teams. Proposed herein is a novel spatial clustering algorithm that automatically finds the projects within a certain budget and spatial constrains. The developed algorithm was successfully validated using 1,800 pavement maintenance projects from two real-life examples of the City of Milton, GA and the City of Tyler, TX.
