Title: Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks,
Abstract: Natural disasters can have catastrophic impacts on the functionality of
infrastructure systems and cause severe physical and socio-economic losses.
Given budget constraints, it is crucial to optimize decisions regarding
mitigation, preparedness, response, and recovery practices for these systems.
This requires accurate and efficient means to evaluate the infrastructure
system reliability. While numerous research efforts have addressed and
quantified the impact of natural disasters on infrastructure systems, typically
using the Monte Carlo approach, they still suffer from high computational cost
and, thus, are of limited applicability to large systems. This paper presents a
deep learning framework for accelerating infrastructure system reliability
analysis. In particular, two distinct deep neural network surrogates are
constructed and studied: (1) A classifier surrogate which speeds up the
connectivity determination of networks, and (2) An end-to-end surrogate that
replaces a number of components such as roadway status realization,
connectivity determination, and connectivity averaging. The proposed approach
is applied to a simulation-based study of the two-terminal connectivity of a
California transportation network subject to extreme probabilistic earthquake
events. Numerical results highlight the effectiveness of the proposed approach
in accelerating the transportation system two-terminal reliability analysis
with extremely high prediction accuracy.