Assessment of urban road resilience to inundation using system function curve: a case study of Fengxi New City, China

The increasing frequency of extreme rainfall events and rapid urbanization have amplified urban flood risks, necessitating advanced tools to assess infrastructure resilience. A dynamic framework integrating Graphics Processing Unit (GPU) Accelerated Surface Water Flow and Transport (GAST) model and system function curves is developed to evaluate road network flood resilience in Fengxi New City, China. High-resolution simulations under rainfall return periods (20–1000 years) quantified temporal degradation of road functionality, revealing nonlinear vulnerability patterns: segments exhibited 55.63% failure under 1000-year events, surpassing node losses (29.2%). Model validation using 2016 flood data showed high accuracy, with a 4.8% average error in inundation area predictions. The framework delineates four resilience phases—prevention, response, recovery, and adaptation—highlighting prolonged waterlogging at critical nodes as a key bottleneck for restoration. By mapping structural vulnerabilities and recovery dynamics, this approach identifies high-risk zones and prioritizes mitigation strategies. The physics-based methodology advances data-driven urban adaptation, offering actionable insights to optimize infrastructure planning under climate change.