Improving Flood Resilience of Bridge Infrastructure through Fluid, Structural, and Risk Modeling

Flood in the Great Lakes area is one of the most common natural hazards, causing significant damage to infrastructure. In pursuit of sustainable and resilient communities against increasingly frequent inclement climate, protecting the lifeline safety of coastal infrastructure becomes imperative. In this paper, we propose a computational framework, which consists of computational fluid dynamics (CFD), finite element analysis (FEA), and Gaussian process regression (GPR) algorithm, to quantitatively assess the responses and risks of bridge, the most vulnerable infrastructure subjected to floods. Based on the simulation results, an integrated tool to enhance the flood resilience of the bridge infrastructure is presented. The outcomes of the study pave the way for coastal communities to estimate the impacts and potential risks confronting the bridge infrastructure during flood hazards. The framework empowers stakeholders in the Great Lakes area to make optimal decisions, implement necessary protective measures, and enhance the overall resilience of the coastal infrastructure vulnerable to flood events.