An Integrated Optimization Model for Life Cycle Pavement Maintenance Budgeting Problems

Abstract

Driven by the demand to preserve the existing road pavement condition, the issue of selecting maintenance action at the appropriate time under budget limitation has attracted great attention from highway agencies. This study focuses on the strategy of how to manage pavement maintenance budget effectively on road network level based on life cycle cost analysis. The framework of resource-constrained project scheduling problem (RCPSP) is implemented to establish a maintenance action decision-making mechanism for allocating pavement maintenance budgets on the planning and controlling phases. In the RCPSP environment, a two-stage optimization model based on constraint programming techniques is developed to meet two different management goals such as 1. annual budget evaluation from planning points of view, and 2. actual budget adjustment from controlling points f view, by considering the factor of road usability. Model-I, the life cycle lifespan evaluation model solves the problem of annual budget evaluation to satisfy the maintenance requirements of all road sections. The optimal maintenance plan then can be made to maintain road performance and evaluate annual budget requirement for future years to maximize total maintenance benefits, in terms of the overall maximum pavement lifespan. Based on the suggested results of budget evaluation from Model-I, Model-II, the actual budget adjustment model deals with the actual budget allocation problem of how to keep up the original maintenance budget plan when the actual budget is always awarded insufficiently each year. Finally, the proposed two-stage integrated models provide an optimal maintenance strategy to respond to actual maintenance status and pavement deterioration as feedbacks to the actual budget adjustment model, and recursively make pavement maintenance strategy closer to actual conditions by budget adjustment yearly.

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

“…To understand the effects of annual budgets, Saitoh and Fukuda defined a set of fuzzy constraints to release the budget requirement when planning M&R treatments [ 35 ]. Similarly, Liu et al developed a model for the budget adjustment purpose also by relaxing the actual budget [ 36 ]. Their research allowed a better M&R decision even when the decision did not strictly satisfy the budget constraints.…”

A method for intelligent generation of decision information for urban road maintenance

“…In recent years, propelled by the rapid evolution of artificial intelligence technology, deep learning techniques have found extensive application in the intelligent identification of road defects [1][2][3][4] , emerging as a significant tool to complement urban road maintenance decision-making [5][6][7][8] . Xiao Liyang et al [9] enhanced the Mask R-CNN model, achieving precise localization and extraction of road surface cracks under high thresholds through a cascade of multiple detectors.…”

The Conceptual of Pavement Management System Based on IoT, Big Data, and Data Mining in Indonesia

“…Thus, these conditions can cause the policyholders to be more optimal in making decisions. Accepted risk, some damaged roads cannot be treated on time, which can accelerate road damage [6]. Meanwhile, several roads were treated repeatedly even though conditions were normal.…”