Event-based methods are commonly used to assess the risk to distributed infrastructure systems. Stochastic event-based methods consider all hazard scenarios that could adversely impact the infrastructure and their associated rates of occurrence. However, in many cases, such a comprehensive consideration of the spectrum of possible events requires high computational effort. This study presents an active learning method for selecting a subset of hazard scenarios for infrastructure risk assessment. Active learning enables the efficient training of a Gaussian process predictive model by choosing the data from which it learns. The method is illustrated with a case study of the Napa water distribution system where a risk-based assessment of the post-earthquake functional loss and recovery is performed. A subset of earthquake scenarios is sequentially selected using a variance reduction stopping criterion. The full probability distribution and annual exceedance curves of the network performance metrics are shown to be reasonably estimated.
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