Incorporating Risk and Uncertainty into Pavement Network Maintenance and Rehabilitation Budget Allocation Decisions

Abstract

According to the American Society of Civil Engineers, 33% of the United States? major roads are in poor or mediocre condition with a projected funding shortfall of $549.5 billion for 2010?2015. Environmental factors, increased traffic, and lack of adequate maintenance are causing many of these roads to deteriorate faster. The imbalance between maintenance needs and available funds tends to become more critical over time, demanding more reliable and advanced tools for allocating funds and prioritizing projects.

In 2012, the U.S. Congress passed the Moving Ahead for Progress in the 21st Century Act (MAP-21) to fund surface transportation programs for 2013?2014 and beyond. MAP-21 establishes a framework for federal transportation investments with the goals of preserving the highway system while improving its condition and performance. This law requires states to develop risk-based asset management plans that include risk management analysis. In order to fulfill MAP-21 requirements, pavement management systems must be upgraded to incorporate risk management, permitting pavement management systems to serve as a more realistic decision support tool for planning and budget allocation in pavement maintenance and rehabilitation.

This dissertation aims to incorporate risk assessment into maintenance and rehabilitation budget decisions at the planning stage. For risk assessment, uncertainty was incorporated into the analysis process, and factors influencing decisions are modeled as probability distributions. The factors included are pavement conditions, available funds, maintenance and rehabilitation costs, and performance prediction. The risk for each scenario is defined as the probability of failing to achieve pre-defined performance goals.

The results of this research show that the benefit-cost budget allocation method has the lowest risk to fail to achieve the performance goals. The maintenance-first method has slightly higher risk but averages scores are better compared with benefit-cost. The method with highest risk is the rehabilitation-first, which have a significant difference with all the other allocation methods.

This research demonstrates that incorporating uncertainty and risk assessment into pavement management can lead to better-informed decision and ultimately improved M&R budget allocation policies. This work provides DOTs with analytical tools and methods for meeting the requirements of MAP-21.