  Resilience has emerged as a crucial concept for evaluating structural
performance under disasters because of its ability to extend beyond traditional
risk assessments, accounting for a system's ability to minimize disruptions and
maintain functionality during recovery. To facilitate the holistic
understanding of resilience performance in structural systems, a
system-reliability-based disaster resilience analysis framework was developed.
The framework describes resilience using three criteria: reliability,
redundancy, and recoverability, and the system's internal resilience is
evaluated by inspecting the characteristics of reliability and redundancy for
different possible progressive failure modes. However, the practical
application of this framework has been limited to complex structures with
numerous sub-components, as it becomes intractable to evaluate the performances
for all possible initial disruption scenarios. To bridge the gap between the
theory and practical use, especially for evaluating reliability and redundancy,
this study centers on the idea that the computational burden can be
substantially alleviated by focusing on initial disruption scenarios that are
practically significant. To achieve this research goal, we propose three
methods to efficiently eliminate insignificant scenarios: the sequential search
method, the n-ball sampling method, and the surrogate model-based adaptive
sampling algorithm. Three numerical examples, including buildings and a bridge,
are introduced to prove the applicability and efficiency of the proposed
approaches. The findings of this study are expected to offer practical
solutions to the challenges of assessing resilience performance in complex
structural systems.
