Urban infrastructure recovery strategy under extreme precipitation based on multi-objective optimization algorithm

Study region Gulou district, Nanjing, China. Study focus Facing escalating extreme precipitation and urbanization, urban infrastructure resilience is critical. While previous recovery strategies often used optimization models, they largely overlooked the underlying mechanisms driving efficient recovery. This study addresses this gap by developing an optimal strategy for post-extreme precipitation infrastructure recovery. We establish a multi-objective optimization model and a novel recovery efficiency index to evaluate recovery combinations, specifically focusing on Gulou District. The analysis considers recovery objectives, infrastructure categories, and spatial distribution to identify efficient recovery methods. New hydrological insights for the region Findings reveal significant spatial and categorical disparities in recovery efficiency, emphasizing the need for context-specific strategies. Optimal recovery methods depend on the interplay between infrastructure type and their geographic clustering. Strategic prioritization of spatially interconnected infrastructure and category-specific recovery sequences can enhance overall efficiency by 15–20 %. These insights provide actionable guidance for policymakers to design resilient recovery plans under climate change pressures. Future refinements will incorporate dynamic interactions between adjacent infrastructure systems to further optimize recovery outcomes.