Risk-Averse Maintenance Decision Framework for Roadside Slopes along Highway Corridors

Abstract

Highway transportation systems are prone to disruptions due to the increasing threat posed by extreme rainfall on the stability of roadside embankments and cut slopes. The failure of roadside slopes leads to system-level traffic disruptions, increases travel times, and demands emergency repairs. Proactively identifying and rehabilitating critical roadside slopes with a limited budget is vital for improving the performance and safety of the highway transportation system. This research aims to create a risk-averse decision framework to support the proactive rehabilitation of roadside slopes with a limited rehabilitation budget. The proposed risk-averse decision will facilitate the proactive slope rehabilitation decisions ensuring the expected generalized cost (i.e., cost of emergency repair and traffic disruption), and conditional value at risk (CVaR) linked to the rehabilitation decision is reduced during extreme precipitation. The proposed rehabilitation decision framework was implemented in a highway network in North Texas. The result shows that the proposed approach identified a list of rehabilitation policies with different expected failure costs and conditional values at risk (CVaR) along the Pareto efficient frontier, thereby enabling transportation agencies to select a rehabilitation strategy at desired risk aversion level. The proposed decision framework aids transportation agencies to make the best use of maintenance funding to improve the serviceability of highway transportation systems during extreme rainfall events.