Prediction of Bridge Inventory Characteristics using Machine Learning

This project will develop a methodology and machine learning tool for predicting key characteristics of bridges in a bridge inventory. The machine learning tool will use individual bridge information available from the National Bridge Inventory (NBI) to predict other bridge characteristics that are better aligned with predictions of current bridge condition ratings and bridge performance in natural hazards. The methodology will help DOTs better quantify the current state of their bridge infrastructure, identify prioritization for modernization and retrofit, and will enable more realistic emergency planning for natural hazards and disasters.

Language

• English

Project

• Status: Active
• Funding: $165000
• Sponsor Organizations:

  Accelerated Bridge Construction University Transportation Center (ABC-UTC)

  Florida International University
  10555 W. Flagler Street
  Miami, FL  United States  33174

  Office of the Assistant Secretary for Research and Technology

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

  University of Washington, Seattle

  Civil and Environmental Engineering Department
  201 More Hall, Box 352700
  Seattle, WA  United States  98195-2700
• Start Date: 20240102
• Expected Completion Date: 20250101
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Asset management; Bridges; Disaster preparedness; Inventory; Machine learning
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways;

Filing Info

• Accession Number: 01924841
• Record Type: Research project
• Source Agency: Accelerated Bridge Construction University Transportation Center (ABC-UTC)
• Files: UTC, RIP
• Created Date: Jul 21 2024 3:09PM