AI-Powered Infrastructure Monitoring and Decision Support for Transportation Safety: Field Deployment and TDI Integration

In its fourth year, this project continues to advance transportation safety through the deployment and scaling of cutting-edge technologies for near real-time monitoring of infrastructure. Building on the foundational work of Year 1 (inverse problem framework development and vision-based structural health monitoring for bridges) and Year 3 (AI-powered video analytics scaled to urban infrastructure), the Year 4 initiative at Howard University integrates three funded years of research into a deployable, scalable monitoring solution. The core focus of Year 4 is field deployment of the integrated monitoring platform on an operational bridge in Washington, DC, identified in coordination with the District Department of Transportation (DDOT). In parallel, the project will conduct exploratory trials of drone-based video and imagery for structural inspection, in collaboration with DDOT’s Drones team, to assess whether this complementary sensing approach warrants full integration in a potential Year 5. The work integrates civil engineering, computer vision, machine learning, and applied mathematics for AI-powered structural diagnosis.

Language

  • English

Project

Subject/Index Terms

Filing Info

  • Accession Number: 01998031
  • Record Type: Research project
  • Source Agency: Sustainable Mobility and Accessibility Regional Transportation Equity Research Center
  • Contract Numbers: 69A3552348303
  • Files: UTC, RIP
  • Created Date: Aug 1 2026 9:43AM