Leveraging AI for Risk‑Informed Culvert Infrastructure Decision‑Making

Culverts are essential components of roadway and drainage systems, yet many are aging, undersized, and vulnerable to failure during heavy rainfall and flooding. Limited inspection resources and outdated condition data make it difficult for transportation agencies to identify which culverts pose the greatest risk to safety and network performance. This project addresses these challenges by applying artificial intelligence and network analysis to improve culvert condition assessment and maintenance prioritization. The research focuses on the Deerfield County Watershed, where publicly available culvert condition data are incomplete or outdated. Machine learning models will be developed to predict current culvert condition ratings using historical records and environmental data, with an emphasis on interpretable and physics-informed approaches. These predictions will be integrated with geospatial network simulations to identify culverts whose failure would result in significant connectivity loss, flooding exposure, or service disruption. Targeted field inspections will be conducted to validate model predictions and improve accuracy. The results will support proactive, data-driven culvert management and provide a scalable framework for applying AI to infrastructure risk assessment.

• Record URL:
  • https://www.umass.edu/neutc/projects/leveraging-ai-risk-informed-culvert-infrastructure-decision-making

Language

• English

Project

• Status: Active
• Funding: $140,000.00
• Contract Numbers:

  69A3552348301

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  University of Massachusetts, Amherst

  Department of Civil and Environmental Engineering
  130 Natural Resources Road
  Amherst, MA  United States  01003
• Performing Organizations:

  University of Massachusetts, Amherst

  Department of Civil and Environmental Engineering
  130 Natural Resources Road
  Amherst, MA  United States  01003
• Principal Investigators:

  Boakye, Jessica

  Asgari, Sadegh

• Start Date: 20260101
• Expected Completion Date: 20261231
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Subprogram: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Culverts; Decision support systems; Machine learning; Maintenance management; Network analysis; Predictive models
• Geographic Terms: Massachusetts
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01974412
• Record Type: Research project
• Source Agency: New England University Transportation Center
• Contract Numbers: 69A3552348301
• Files: UTC, RIP
• Created Date: Dec 18 2025 2:26PM