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Resilience and sustainability are critical yet often distinct attributes in evaluating the performance of underground infrastructure. Traditionally, resilience has focused on functionality and network robustness, while sustainability has prioritised cost efficiency and individual project viability. However, recent developments reveal increasing overlaps between these two concepts, as both now integrate structural analysis with socio-economic factors, employ multi-hazard scenario forecasting for risk assessment, and adopt lifecycle-based decision-making. Despite these advances, developing a quantitative method to simultaneously assess resilience and sustainability remains challenging. The paper aims to advance infrastructure planning by enabling systematic, lifecycle-informed decision-making for resilient and sustainable underground systems. An integrated framework is proposed to evaluate infrastructure resilience and sustainability under multi-hazard conditions. First, lifecycle performance under design hazard scenarios is predicted using a modular numerical approach. Event trees are then constructed to analyse multiple performance paths, enabling comparison of hazard risks and recovery strategies. The model captures functionality metrics and post-hazard damage accumulation over time, which are visually represented through event trees. Maintenance and restoration timelines, costs, and socio-economic impacts are quantified through correlating deterministic formulas and physics-based performance path to minimise subjectivity. To demonstrate its applicability, this framework is applied to an urban rectangular tunnel under two multi-hazard scenarios. Results show that excessive repair measures ensure resilience but introduce redundancy and waste, whereas minimal investment reduces costs but increases safety risks. The framework proposed in this paper enables systematically balances safety and cost-effectiveness, integrating resilience-based assessments to determine functionality baselines with sustainability-based evaluations to establish budgetary constraints, thereby facilitating identification of optimal solutions.
Zhu et al. (Sat,) studied this question.