OR/MS Methods in Infrastructure Management

SD34.1 Infrastructure State Transition Probability Computation using Duration Models

• Rabi Mishalani; Ohio State University, Dept. of Civil & Environ. Eng., Columbus, OH 43210; mishalani.1@osu.edu
• Samer Madanat; University of California, Dept. of Civil & Environ. Eng., 114 McLaughlin Hall, Berkeley, CA 94720; madanat@ce.berkeley.edu

State-based infrastructure deterioration models consist of transition probabilities capturing the evolution of condition over time. Current methods for determining such probabilities suffer from several serious limitations. An alternative approach is presented where time-based duration models are used. The methodology is demonstrated using a reinforced concrete bridge deck data set.

SD34.2 Determining the Value of the Air Traffic Infrastructure

• Robert B. Rovinsky; Federal Aviation Administration, 800 Independence Ave. SW, Washington, DC 20591; robert.rovinsky@faa.gov

The Civil Engineering Society recently rated the nation's air traffic control infrastructure as poor. Since airspace availability exceeds 99.9%, justifying additional funding is difficult. The FAA is connecting its infrastructure to services and determining 'derived value,' thus creating a business case to modernize and evolve its infrastructure.

SD34.3 Transportation Asset Management under Uncertainty

• Jeffrey Fine; Northwestern University, IEMS Dept., Evanston, IL 60208; j-fine@northwestern.edu
• Thanasis Ziliaskopoulos; Northwestern University, CEE Dept., Evanston, IL 60208; a-z@northwestern.edu

Transportation infrastructure problems, in particular physical assets, are modeled effectively as capital investment problems. We discuss issues of uncertainty of the value of the asset, of increasing uncertainty with respect to time, the use of assets as insurance, present and future values/utilities associated with the asset, multiple time/exercise opportunities, and risk-neutral/averse assumptions. Financial models are fundamentally concerned with long-term results and the long-term behavior of assets...

SD34.4 Optimal Infrastructure Facility Maintenance & Repair Policies under Uncertainty in Deterioration: An Adaptive Control Approach

• Pablo L. Durango-Cohen; University of California, IEOR Dept., Berkeley, CA 94720-1777; durango@ieor.berkeley.edu
• Samer Madanat; University of California, Dept. of Civil & Environ. Eng., 114 McLaughlin Hall, Berkeley, CA 94720; madanat@ce.berkeley.edu

Agencies must often develop infrastructure M&R policies with limited information about facility deterioration. We will present 2 AC models that explicitly account for the uncertainty in characterizing a facility's actual deterioration rate. These methods use infrastructure condition data obtained during operation of a facility to improve the characterization over a finite planning horizon through Bayesian updating. We show that economic benefits can be achieved.