Data-Driven Inspection Planning for Utah Culverts Using Federated Learning

As transportation agencies increasingly adopt cutting-edge data analytics to refine infrastructure management strategies, the role of condition prediction models is becoming more critical. These models are pivotal in optimizing maintenance budgets, especially for underrepresented infrastructures like culverts, which have been neglected in the past. Similarly, due to the lack of a comprehensive culvert management system, the Utah Department of Transportation (UDOT) faces significant challenges in inspecting and maintaining culverts. Therefore, this study proposed a data-driven approach using federated learning to enhance Utah's culvert management. Since Utah's culvert dataset was limited, we expanded it by collecting data from several other state DOTs. However, to address data privacy concerns, we employed federated learning approach. This innovative technique avoids direct data sharing. Instead, each DOT trains a local model on its own data, and only the updated model parameters are shared with UDOT. This allows us to leverage the collective knowledge of multiple DOTs while ensuring robust data security. Our findings highlight the efficacy of the proposed federated learning-based models in enhancing prediction accuracy while ensuring data privacy and reducing data transmission overheads.

• Record URL:
  https://www.ctips.org/projects/details.php?id=603

Language

• English

Project

• Status: Active
• Funding: $100000
• Contract Numbers:

  69A3552348308

• 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:

  Center for Transformative Infrastructure Preservation and Sustainability

  North Dakota State University
  Fargo, ND  United States  58108
• Project Managers:

  Tolliver, Denver

• Performing Organizations:

  University of Utah

  Department of Civil and Environmental Engineering
  110 Central Campus Drive Suite 2000
  Salt Lake City, UT  United States  84112
• Principal Investigators:

  Rashidi, Abbas

• Start Date: 20240506
• Expected Completion Date: 20260505
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Source Data: CTIPS-005

Subject/Index Terms

• TRT Terms: Artificial intelligence; Budgeting; Culverts; Data privacy; Inspection; Predictive models; Training
• Geographic Terms: Utah
• Subject Areas: Administration and Management; Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation; Planning and Forecasting;

Filing Info

• Accession Number: 01918357
• Record Type: Research project
• Source Agency: Center for Transformative Infrastructure Preservation and Sustainability
• Contract Numbers: 69A3552348308
• Files: UTC, RIP
• Created Date: May 14 2024 3:27PM