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SWUTC Ph.D. Candidate Assistantship Project Description

Incorporating Risk and Uncertainty into Pavement Network Maintenance and Rehabilitation Budget Allocation Decision-Making

University: Texas A&M University

Principal Investigator:
Jose Menendez
Zachry Department of Civil Engineering
(979) 458-4742

Faculty Supervisor:
Nasir Gharaibeh
Zachry Department of Civil Engineering
(979) 845-3362

Funding Source: USDOT Funds

Total Project Cost: $36,279

Project Number: 600451-00037

Date Started: 9/1/13

Estimated Completion Date: 12/31/14

Project Summary

Project Abstract:
For many years, DOTs have been using deterministic pavement management systems (PMSs) to support the decision-making process of defining when, where, and how M&R activities should take place. Network-level PMSs currently do not consider risk and uncertainty in this decision-making process, so they need to be upgraded in order to fulfill the MAP-21 requirement and to become more effective decision-support tools.

This dissertation focuses on incorporating risk assessment into different policies for allocating the M&R budget. Relevant factors that affect decisions will be modeled as probability distribution functions in order to incorporate uncertainty in these factors into the analysis process. These factors are pavement condition data, funding levels, maintenance costs, and performance prediction models. The risk level for each budget allocation scenario will be expressed as the probability of failing to achieve a target network condition.

This research will demonstrate that incorporating uncertainty and risk analysis in pavement management can lead to better-informed decision makers and ultimately better M&R budget allocation policies. Additionally, this work provides DOTs with analytical tools and methods for meeting the requirements of MAP-21.

Project Objectives:
The purpose of this research is to develop a methodology that incorporates risk and uncertainty into pavement M&R budget allocation decisions at the network level. The specific objectives of this research are:

  • To define the uncertainties in key input data (condition scores, budget, and M&R unit costs) and in the pavement performance predictions;
  • To develop a computational model that simulates this decision-making process and optimizes M&R project selections, considering uncertainties in critical input parameters and performance predictions.
  • To assess the risk of failing to achieve the target pavement network condition (measured in terms of pavement condition indicators such as percent lane-miles in adequate condition and average network condition).

Task Descriptions:

Task 1Conduct Literature Review

Task 2Determine Uncertainty in Key Input Data

Task 3Develop an Optimization Model for Prioritizing Maintenance and Rehabilitation Projects

Task 4Assess Risk of Failure to Achieve Target Network Condition

Task 5 – Prepare Final Report