With the aging transportation network in the U.S. and stringent funds, there is a greater need for robust risk-based transportation asset management (TAM) approaches. To date, several studies have incorporated infrastructure performance uncertainty in infrastructure management. These studies focused on minimizing the uncertainty given a target level of performance at the network level. Observing the success of quantitative risk analysis (QRA) in investment strategies in the world of finance and economics, coupling risk and uncertainty with return to evaluate competing investment strategies can diversify the agency choices and allow for a wider range of strategies. This study presents a preliminary framework to use QRA for budget allocation within a pavement management system. An implementation is presented for a sample pavement network to quantify the impact of preservation activities and the risks associated to different budget allocation strategies. The analysis was conducted using @Risk, which performed Monte Carlo simulation to assess the uncertainty of the network performance. The analysis produced an empirical efficient frontier with Pareto optimal strategies in the risk-return space.
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