Leveraging Digital Twins for Bridge Maintenance Prioritization following Coastal Flood Events

Abstract

Abstract Bridges are critical to the US transportation network and are essential components of our infrastructure systems. Coastal floods can disrupt these structures, isolating communities and highlighting the urgent need for effective maintenance and prioritization strategies. This study introduces a digital twin framework that evaluates the impacts of coastal floods on bridge substructures by integrating multisource data and employing spatial algorithms like inverse distance weighting and k - d tree methods to model flood severity. Using a generative adversarial network (GAN) with focal loss, the framework simulates preflood and postflood scenarios, generating synthetic data sets that reflect realistic damage patterns and evolving maintenance priorities. The GAN-enhanced model improved F1 scores (the harmonic mean of precision and recall, providing a balance between the two metrics), particularly for Condition 4 (bridges with advanced section loss, deterioration, spalling, or scour), where the score increased from 0.63 to 0.87 preflood and from 0.63 to 0.88 postflood, effectively addressing class imbalances. It also enhanced macro and weighted average F1 scores, demonstrating robust predictive capabilities. Confusion matrices showed increased identification of bridges in Conditions 4 and 5, consistent with the model’s emphasis on vulnerable infrastructure. The study’s findings reveal shifts in maintenance priorities when flood severity is incorporated, emphasizing bridges with higher susceptibility to combined flooding and socioeconomic impacts. While the framework primarily focused on physical infrastructure impacts, it also highlighted potential disparities in resource allocation, suggesting the need for further research into equitable distribution strategies. The digital twin provides actionable insights for local and federal agencies, offering a valuable tool for managing and prioritizing infrastructure maintenance in response to extreme weather events while laying the groundwork for more inclusive resource planning.