Efficient seismic reliability and fragility analysis of lifeline networks using subset simulation

TL;DR

This paper introduces a variance-reduction subset simulation method for efficient seismic reliability analysis of lifeline networks, accounting for complex topologies and interdependencies to accurately estimate network fragility.

Contribution

It reformulates network limit-state functions into piecewise continuous forms and links intermediate failure events to seismic intensity, enabling rapid fragility curve estimation.

Findings

Effective evaluation of large-scale network fragility.

Significant reduction in computational effort.

Accurate system-level fragility assessment.

Abstract

Various simulation-based and analytical methods have been developed to evaluate the seismic fragilities of individual structures. However, a community's seismic safety and resilience are substantially affected by network reliability, determined not only by component fragilities but also by network topology and commodity/information flows. However, seismic reliability analyses of networks often encounter significant challenges due to complex network topologies, interdependencies among ground motions, and low failure probabilities. This paper proposes to overcome these challenges by a variance-reduction method for network fragility analysis using subset simulation. The binary network limit-state function in the subset simulation is reformulated into more informative piecewise continuous functions. The proposed limit-state functions quantify the proximity of each sample to a potential network failure domain, thereby enabling the construction of specialized intermediate failure events, which can be utilized in subset simulation and other sequential Monte Carlo approaches. Moreover, by discovering an implicit connection between intermediate failure events and seismic intensity, we propose a technique to obtain the entire network fragility curve with a single execution of specialized subset simulation. Numerical examples demonstrate that the proposed method can effectively evaluate system-level fragility for large-scale networks.