Network-level pavement maintenance and rehabilitation decision-making with the optimized annual budget

Abstract

Maintenance and rehabilitation (M&R) is necessary to keep pavement networks in good condition. Due to the capital intensity, M&R funding is always insufficient. The annual budget, determining the available funding, is a critical criterion when planning M&R treatments. However, its values are often given, and the determination of the values is seldom discussed. To fill the gap, this paper focuses on both the determination of annual budgets and the budget allocations, and therefore enhances the network-level decision-making on M&R by developing a Multi-Objective Optimization (MOO) method. This method does not only optimize and trade off the annual budgets and their consequences, but also allocates the funding across the entire network through generating the optimized M&R decisions. According to a case study with 50 segments, the developed method successfully and effectively identified non-linear discrete relationship between the minimized annual budgets and the maximized M&R benefits subject to all the constraints, and generated the optimized annual budget allocation for each M&R decision. The achievements of this paper can be used to enhance the efficiency of M&R decisions and contribute to informed pavement management.