Abstract

Escalating threats from natural disasters and technical failures underscore the critical role of infrastructure resilience in sustaining operations and reducing losses. Existing studies have advanced resilience optimization, but still provide limited explanation of how resilience improvements are converted into stakeholder-specific gains or how such heterogeneous gains can guide strategy prioritization. This study aims to develop a gain-oriented infrastructure resilience optimization framework that explicitly links resilience strategies, resilience increments, stakeholder-specific gains, stakeholder groups, and aggregated system-level benefits. The framework is operationalized through a fuzzy-evidential Bayesian Network (FEBN) parameterized by triangular fuzzy numbers, Dempster–Shafer evidence theory, and Noisy-Or model, complemented by Dynamic Bayesian Network (DBN) analysis to simulate long-term optimization dynamics. A case study of Nanjing Metro Line 7 demonstrates the framework’s applicability. The results show that proactive strategies, particularly component reliability design (RS 1-1) and emergency response planning (RS 4-1), are high-leverage interventions for improving aggregated gain. Failure-scenario, parameter-perturbation, and baseline-comparison analyses indicate that the main prioritization results remain broadly stable under uncertainty and consistent with non-evidence BN baselines. The DBN results further reveal that operator-centered optimization improves aggregated gain over time but may produce asymmetric benefits. Overall, the framework supports transparent, stakeholder-oriented resilience strategy prioritization in socio-technical infrastructure systems.

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