Efficient System Reliability-Based Disaster Resilience Analysis of Structures Using Importance Sampling

Abstract

Disaster resilience is an emerging concept for managing the risk of civil structural systems considering not only structural safety against extreme loads but also aftermath recovery efforts. The system reliability-based resilience analysis framework facilitates the quantitative evaluation of the disaster resilience performance of structural systems by assessing reliability and redundancy for numerous disruption scenarios. However, its practical application is limited due to the substantial number of structural analyses required to estimate the reliability and redundancy indices. To address the computational challenges, this paper proposes a new importance sampling algorithm, termed Importance Sampling for Noteworthy Scenarios (ISNS). The ISNS algorithm leverages a Gaussian process-based principal point search method to identify initial disruption scenarios that dominantly impact the structure's resilience. Subsequently, a mixture-based distribution is constructed to represent the near-optimal importance sampling density characterizing the failure domains of the noteworthy disruption scenarios. The two-step procedure enables the simultaneous estimation of both reliability and redundancy indices of the identified noteworthy scenarios. Furthermore, an active learning scheme is incorporated to efficiently train surrogates. Numerical examples of engineering applications are investigated to demonstrate the improved efficiency offered by the proposed method. However, the proposed method faces limitations in multihazard contexts and high-dimensional, highly nonlinear scenarios. These limitations necessitate further validation of the Gaussian process and mixture models to ensure robustness.