Active learning method for risk assessment of distributed infrastructure systems. (12th February 2021)

• Record Type: Journal Article
• Authors: Tomar, Agam; Burton, Henry V.
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 36:Number 4(2021)
• Page Start: 438
• Page End: 452
• Publication Date: 2021-02-12
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12665
• Languages: English
• ISSNs: 1093-9687

Abstract

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.