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Active learning method for risk assessment of distributed infrastructure systems
Author(s) -
Tomar Agam,
Burton Henry V.
Publication year - 2021
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/mice.12665
Subject(s) - computer science , hazard , event (particle physics) , variance reduction , variance (accounting) , process (computing) , risk assessment , gaussian process , hazard analysis , reduction (mathematics) , gaussian , data mining , reliability engineering , engineering , mathematics , computer security , chemistry , physics , accounting , organic chemistry , quantum mechanics , business , operating system , geometry
Abstract 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.