Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks

Assessment of the impact of natural disasters on infrastructure systems is of importance toward four main objectives: (1) Planning for actions that eliminate or reduce the long-term risk to human life and infrastructure systems (e.g.[2]); (2) Disaster preparation or adjustment, which aims to reduce the risk of damages and injuries while enabling the capability to cope with the temporary disruption of the infrastructure systems (e.g.[3]); (3) Development of effective emergency response strategies (e.g.[4]); and (4) Post-disaster recovery planning (e.g.[5]). These four are, respectively, known as the mitigation, preparedness, response, and recovery practices. A variety of analytical [6], simulation [7-11], and optimization [12] approaches are proposed in the literature for hazard reliability analysis of infrastructure systems. A comprehensive literature review on transportation infrastructure system performance in disasters is provided in [13]. Simulation-based reliability assessment of large infrastructure systems are often computationally intractable or expensive due to the large number of network components, complex network topology, statistical dependence between component failures, and uncertainties in the hazard models. This will impose limitations on design optimization or sensitivity analysis of these systems. Alternatively, a more efficient response assessment for large infrastructure systems can be made possible by using approximate surrogates [14]. Surrogates are fast models that approximately describe the relationship between the system inputs and outputs and serve as a substitute for more expensive simulation tools. If the response evaluated by the reference expensive model is denoted by f (x), a surrgate seeks to provide a global approximate function f (x).