An optimization-based decision support framework for coupled pre- and post-earthquake infrastructure risk management

In the context of infrastructure resilience and risk management, key decisions occur in the aftermath of adverse events (e.g., immediate response and later repair of damage), but preemptive decisions must be made under uncertainty about the specific disaster realization to face in the future (e.g., strengthening components, or allocating resources for post-event activities). This paper proposes an optimization framework to address problems in which preemptive decisions are coupled with those eventually required to respond to an uncertain adverse event. Specifically, strategic decisions are pursued regarding whether to proactively retrofit or reactively repair bridges in a transportation network under seismic hazards, with the objective of minimizing the cost of maintaining a target network performance metric throughout a set of possible adverse scenarios. A two-stage stochastic programming approach is presented, which relates pre- and post-event decisions, accounting for the uncertainty throughout scenarios. The proposed approach implies a decomposition of the optimization problem that enables the analysis of large sets of scenarios, which is advantageous when dealing with complex networks as the ones addressed in infrastructure engineering practice. The methodological framework is presented along with an analysis of the San Francisco Bay Area transportation network, as an instance of a realistic, complex infrastructure network. Results evidence the potential of the approach to provide risk-informed decision support, while being able to deal with large sets of components and scenarios under an exact optimization approach, and solving problems with large number of variables and constraints.