Skip to main content
Open Access Publications from the University of California

UC Berkeley

UC Berkeley Electronic Theses and Dissertations bannerUC Berkeley

Incorporating Network Considerations into System-level Pavement Management Systems

  • Author(s): Medury, Aditya
  • Advisor(s): Madanat, Samer
  • et al.

The objective of transportation infrastructure management is to provide optimal maintenance, rehabilitation and replacement (MR&R) policies for a system of facilities over a planning horizon. While most approaches in the literature have studied it as a finite resource allocation problem, the presence of an underlying network configuration has been largely ignored. The recognition of the network configuration introduces several challenges, as well as opportunities, for system-level MR&R decision-making, which cannot be adequately handled by the existing decision-making frameworks.

This dissertation focuses on furthering the development of Markov decision process (MDP)-based system-level MR&R decision-making frameworks. In particular, two problems of interest are identified. The first problem concerns itself with identifying an optimal system-level optimization approach for solving budget allocation problems. The second problem of interest involves moving beyond traditional budget allocation problems to incorporate network considerations into system-level decision-making.

In the first part of the dissertation, a revised MDP-based optimization framework is proposed for solving the budget allocation problem. The framework, referred to as simultaneous network optimization (SNO), combines the salient features of the different MDP-based optimization approaches in infrastructure management literature, and provides optimal facility-specific MR&R policies for budget allocation problems. The proposed methodology is then compared with the other state-of-the-art MDP methodologies using a parametric study involving varying system sizes. The results of the study indicate the SNO outperforms the other MDP-based optimization frameworks.

In the second part of the dissertation, it is argued that while SNO is optimal for solving budget allocation problems, it can produce sub-optimal policies upon introducing network constraints. Consequently, the use of an approximated dynamic programming (ADP) framework is motivated to solve system-level MR&R decision-making problems involving network constraints. ADP facilitates the modeling of complex problem formulations by overcoming the curse of dimensionality associated with traditional dynamic programming frameworks.

To assess the suitability of ADP for system-level infrastructure management, two scenarios involving network considerations are investigated. In the first scenario, an approximate dynamic programming framework is proposed, wherein capacity losses due to construction activities are subjected to an agency-defined network capacity threshold. A parametric study is conducted on a stylized network configuration to infer the impact of network-based constraints on the decision-making process. The results indicate that ADP performs better than SNO when the network capacity constraints are binding on the decision-making process.

In the second scenario, the impact of introducing economies of scale (EOS) within budget allocation problems is investigated. Herein, incorporating network considerations leads to economic interdependence, wherein potential cost savings can be achieved by combining MR&R activities across adjacent road sections. Using parametric case studies, it is observed that the performances of ADP and SNO are comparable, with ADP improving upon the results of SNO under low budget and high EOS settings.

In conclusion, the findings from this dissertation indicate that ADP is a robust modeling framework for MDP-based infrastructure management problems. While previous research illustrates the use of ADP in solving system-level budget allocation problems, it is shown here that ADP is more relevant for modeling problems involving complex inter-facility dynamics. In particular, ADP is most beneficial in scenarios wherein finding optimal policies using analytical frameworks is not feasible.

Main Content
Current View