Asset Management of Bridges Using Uncrewed Aerial Vehicles and Machine Learning Models

Bridges play a key role in supporting the transportation network in the United States. The 2021 infrastructure report card prepared by ASCE highlighted that more than 40% of bridges in the U.S. are over 50 years old. Some of these bridges are classified as structurally deficient, even though they are safe to travel. To address these challenges, highway agencies are exploring innovative technologies to conduct inspections and realize benefits in terms of access, cost, and safety. Federal and state DOTs have conducted several studies on the application of uncrewed aerial vehicles (UAVs) for bridge health monitoring. Digital twins of bridges will be very beneficial in predicting the long-term performance of the bridge infrastructure assets. However, there are challenges for building the digital twins due to the knowledge gap on (1) a framework for conducting 360° bridge inspections using UAVs and (2) integrating that information into building information modeling (BIM) platforms. The goal of this project is to demonstrate the framework for conducting 360° bridge inspections developed by the principal investigators (PI), develop machine learning models to extract the condition information of a bridge element, and develop a framework to integrate that information into BIM models. The research output will assist the DOTs in understanding the bridge infrastructure condition and making informed decisions about preventive maintenance. This project is thus directly related to National Center for Infrastructure Transformation's (NCIT’s) focus area of “Improving the Durability and Extending the Life of Transportation Infrastructure” and in particular to the NCIT’s topical pillar: Technology.

Language

• English

Project

• Status: Active
• Funding: $300000
• Contract Numbers:

  69A3552344813

• 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:

  National Center for Infrastructure Transformation

  Prairie View A&M University
  Prairie View, TX  United States  77446
• Performing Organizations:

  Michigan State University, East Lansing

  Department of Civil & Environmental Engineering
  Institute for Community Development
  East Lansing, MI  United States  48824-1226
• Principal Investigators:

  Congress, Surya Sarat Chandra

  Cetin, Bora

  Puppala, Anand

  Cetin, Kristen

• Start Date: 20230901
• Expected Completion Date: 20250831
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program
• Source Data: 01-06-MSU

Subject/Index Terms

• TRT Terms: Asset management; Bridges; Building information models; Drones; Information processing; Machine learning; Structural health monitoring
• Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation;

Filing Info

• Accession Number: 01893158
• Record Type: Research project
• Source Agency: National Center for Infrastructure Transformation
• Contract Numbers: 69A3552344813
• Files: UTC, RIP
• Created Date: Sep 13 2023 12:06PM