When Roads Go Underwater: AI-Enhanced Digital Twin–Driven Flood Resilience for Roadway Serviceability Assessment

Abstract

Extreme flooding poses escalating risks to roadway infrastructure, threatening structural integrity, operational reliability, and public safety. Existing flood-monitoring approaches primarily utilize sparse sensor networks, thus providing limited real-time data and insufficiently capturing indirect flooding impacts on noninundated roadway segments. This gap complicates emergency response, delays evacuation, and undermines postevent recovery efforts. Addressing this critical knowledge gap, this study proposes an advanced predictive decision-support framework leveraging an artificial intelligence (AI)-enhanced digital twin integrated with flood simulations and graph neural network (GNN) modeling. It systematically assesses roadway serviceability during extreme flooding by integrating structural conditions, operational disruptions, historical maintenance records, inundation severity, and recovery timelines. By consolidating heterogeneous data sets, including historical traffic volumes, pavement conditions, hydrological data, and weather forecasts, the developed framework provides accurate, real-time predictive insights for both inundated and indirectly impacted roadway segments. This capability was demonstrated through an illustrative validation study. Ultimately, this research equips transportation agencies and emergency responders with robust hands-on tools that can facilitate optimized emergency response, improved infrastructure management, and enhanced resilience of transportation systems under ever-increasing flood risks due to climate change.