Risk-based maintenance and rehabilitation decisions for transportation infrastructure networks Academic Article

abstract

• A method for determining optimal risk-based maintenance and rehabilitation (M&R) policies for transportation infrastructure is presented. The proposed policies guarantee a certain performance level across the network under a predefined level of risk. The long-term model is formulated in the Markov Decision Process framework with risk-averse actions and transitional probabilities describing the uncertainty in the deterioration process. The well known Conditional Value at Risk (CVaR) is used as the measure of risk. The steady-state risk-averse M&R policies are modeled assuming no budget restriction. To address the short-term resource allocation problem, two linear programming models are presented to generate network-level polices with different objectives. While the proposed methodology is general and can be used with any performance indicator, pavement roughness is used for numerical studies and an analytical expression for computing CVaR is derived. 2010 Elsevier Ltd. All rights reserved.

authors

• Butenko, Sergiy
• Damnjanovic, Ivan

published proceedings

• TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE

author list (cited authors)

• Seyedshohadaie, S. R., Damnjanovic, I., & Butenko, S.

citation count

• 41

complete list of authors

• Seyedshohadaie, S Reza||Damnjanovic, Ivan||Butenko, Sergiy

publication date

• January 2010

publisher

• Elsevier  Publisher

published in

• Transportation Research Part A Policy and Practice  Journal

keywords

• Cvar
• Maintenance
• Rehabilitation
• Resource Allocation
• Risk

Digital Object Identifier (DOI)

• 10.1016/j.tra.2010.01.005

start page

• 236

end page

• 248

volume

• 44

issue

• 4