Data-Driven Inspection Planning for Utah Culverts Using Federated Learning

In recent years, transportation agencies have increasingly turned to machine learning (ML) to enhance the effectiveness of infrastructure asset management. However, limited local inventory data often hinders building accurate and reliable ML models. Additionally, data privacy and ownership concerns discourage agencies from sharing raw datasets. Many state departments of transportation (DOTs), including the Utah DOT (UDOT), face challenges in managing culverts due to limited inspection data and privacy concerns. This research proposes using federated learning (FL), an emerging ML paradigm, to enhance culvert condition prediction without requiring centralized data sharing. By leveraging FL, UDOT can collaboratively train predictive models with data from other state DOTs while preserving data confidentiality. The project involves collecting culvert and environmental data from multiple state inventories, preprocessing them to ensure consistency, and developing artificial neural network (ANN)-based models within the FL framework. The resulting FL model achieved an accuracy of 80.4%, performing comparably to the centralized model trained on the fused dataset and significantly outperforming the model trained solely on Utah’s data. The findings demonstrate that FL can effectively support high-performance predictive modeling while preserving data. This research offers a novel approach to infrastructure asset management that balances predictive accuracy with regulatory compliance, setting a precedent for broader adoption of FL in transportation systems.

• Record URL:
  • https://www.ugpti.org/resources/reports/details.php?id=1259
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/86851
• Summary URL:
  • https://rosap.ntl.bts.gov/view/dot/87063
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Center for Transformative Infrastructure Preservation and Sustainability

  North Dakota State University
  Fargo, North Dakota  United States  58108-6050

  University of Utah, Salt Lake City

  Department of Civil and Environmental Engineering
  Salt Lake City, UT  United States 

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Federal Highway Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590

  Utah Department of Transportation

  Salt Lake City, UT  United States 
• Authors:
  • Mohammadi, Pouria
  • 0000-0002-8373-9606
  • Rashidi, Abbas
  • 0000-0002-4342-0588
• Publication Date: 2025-8

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Photos; References; Tables;
• Pagination: 31p

Subject/Index Terms

• TRT Terms: Asset management; Culverts; Data fusion; Data privacy; Inspection; Machine learning; Neural networks; Predictive models
• Geographic Terms: Utah
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01968430
• Record Type: Publication
• Report/Paper Numbers: CTIPS-25-001
• Contract Numbers: CTIPS-005; 69A3552348308
• Files: UTC, NTL, TRIS, USDOT, STATEDOT
• Created Date: Oct 13 2025 4:31PM