Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Howard, Isaac L.

Committee Member

Priddy, Lucy P.

Committee Member

Wang, Jun

Committee Member

Bourgeois, Angi E.

Date of Degree

11-25-2020

Document Type

Dissertation - Open Access

Major

Civil Engineering

Degree Name

Doctor of Philosophy

College

James Worth Bagley College of Engineering

Department

Department of Civil and Environmental Engineering

Abstract

The U.S. Air Force (USAF) estimates it has a $33 billion (about 10 percent is airfield pavements) deferred maintenance backlog within its $263 billion infrastructure portfolio. Given the scope of this backlog and the importance of airfields, the USAF has a vested interest in finding strategies to help reverse this growing trend. Without an increase in funding, divestiture of excess infrastructure, or change in strategy, this backlog is estimated to climb to over $50 billion by 2030. Reversing the growing infrastructure backlog trend requires new methods and strategies to rethink how the USAF invests in its infrastructure. As such, the overall goal of this research is to develop a comprehensive and practical asset management approach to reduce the total cost of ownership of USAF airfield pavements. By reducing the cost of ownership, the goal is to reverse the growing maintenance backlog while maintaining a pavement portfolio capable of supporting USAF flying operations into the future. While this research is particularly relevant to the USAF, it seeks to fill research gaps within the current body of knowledge related to pavement management strategies for other agency types by presenting a practical, simulation-based methodology for work planning and budget allocation across a large pavement portfolio over a thirty-year period. The dissertation presents the development of the BEAST and RAMPSS algorithms. The BEAST algorithm is a simulation tool capable of modeling behaviors and decisions of 109 organizations managing a global network of airfield pavements over thirty years. Additionally, the BEAST is used to forecast outcomes of USAF investment decisions utilizing its current management strategies and historical behaviors. The RAMPSS is a simulation algorithm designed to select the most economical maintenance strategy for each pavement section in the USAF’s portfolio (i.e., individualized maintenance recommendation strategy for each pavement section). Analysis from the RAMPSS algorithm of the USAF’s pavement portfolio suggests that airfields are generally more cost-effective to maintain if kept in better conditions with strategies other than localized preventative maintenance. The USAF’s current maintenance strategy is unsustainable; however, switching to recommendations from RAMPSS (incorporated and modeled in the BEAST) provides a potentially significant course correction.

URI

https://hdl.handle.net/11668/20816

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