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There exist pressing research needs for developing sophisticated methodologies for probabilistic seismic risk assessment (PSRA) of complex lifeline networks, which are critical backbones of today's urban communities. In particular, ground-motion intensities at different locations of lifeline networks show significant variability and spatial-correlation, which should be properly considered in evaluating structural damage of lifeline networks and their corresponding network-level performance. In addition, the probabilities of the post-disaster states of the network often shows large variability, so simulation-based approaches may require a huge number of computational simulations if the set of the possible network states includes rare events. To improve the efficiency of simulation-based approach while maintaining its broad applicability, adaptive importance sampling methods have been recently introduced. In this study, a new PSRA approach is developed for lifeline networks by extending a cross-entropy-based adaptive importance sampling (CE-AIS) to estimate the probabilities of multiple possible states of the network utility concurrently. To test and demonstrate the proposed approach, a hypothetical traffic network under seismic hazard from surrounding active faults is investigated. The results of the numerical example show that the proposed approach, termed as CE-based concurrent AIS (CE-CAIS), identifies a near-optimal sampling density through only a few rounds of pre-sampling and drastically improves efficiency of simulation-based PSRA. It is also noted that CE-CAIS does not rely on a subjective intuition to select importance sampling density or require additional reliability analysis to identify important regions. The proposed method is expected to improve PSRA of lifeline networks in terms of efficiency and applicability, and provide new insights into the risk assessment and management of such urban infrastructures.

Cross-entropy-based Adaptive Importance Sampling for Probabilistic Seismic Risk Assessment of Lifeline Networks Considering Spatial Correlation